{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "modulationClassify",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk7iF8a-iQWD",
        "colab_type": "code",
        "outputId": "ab25c6c1-8fc1-456a-b568-1514a6d79f5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#!/usr/bin/python\n",
        "# coding = utf-8\n",
        "\n",
        "import numpy as np\n",
        "from keras import metrics, regularizers, backend\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import random as rn\n",
        "import os\n",
        "import json\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras import metrics, regularizers, optimizers, backend\n",
        "from keras.callbacks import TensorBoard, EarlyStopping\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, Flatten, pooling\n",
        "from keras.utils import np_utils, vis_utils\n",
        "from keras import metrics, regularizers, optimizers, backend\n",
        "from keras.callbacks import TensorBoard, EarlyStopping\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense, LSTM, CuDNNLSTM, Flatten, Dropout, BatchNormalization\n",
        "from keras.utils import np_utils, vis_utils"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X2JKGpQVmu75",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7p8x1Wjb9WG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b7029016-fb60-452d-d720-65afc498d17a"
      },
      "source": [
        "# load train data\n",
        "    #高阶累积量维度\n",
        "    Nfeature = 9\n",
        "    #机器学习分类问题共三类\n",
        "    NClass = 3\n",
        "    #每类的训练序列个数\n",
        "    NTrain = 1000\n",
        "    #导入训练集\n",
        "    train_data = np.loadtxt('./train_modulation.csv', delimiter = ',', dtype = float)\n",
        "    #输出正例\n",
        "    ydata = train_data[:,-1]\n",
        "    #输入的高阶累积量\n",
        "    xdata = np.delete(train_data, -1, axis=1)\n",
        "    index = np.arange(ydata.shape[0])\n",
        "    #打乱数据集的顺序\n",
        "    np.random.shuffle(index)\n",
        "    xdata = xdata[index,:]\n",
        "    ydata = ydata[index]\n",
        "    print(ydata)\n",
        "    type(ydata)\n",
        "    #ydata = np_utils.to_categorical(ydata, NClass)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 2. 1. ... 2. 1. 1.]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPjD5LobcGL3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9a27a3da-f00e-429a-ec75-d333c74d1e3d"
      },
      "source": [
        "print(ydata)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1. 0. 1. ... 0. 0. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9u-W8seNiU5s",
        "colab_type": "code",
        "outputId": "42c5f7e1-8811-44a1-d653-83f971f6cbdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "if __name__ == '__main__':\n",
        "\n",
        "    # load train data\n",
        "    #高阶累积量维度\n",
        "    Nfeature = 9\n",
        "    #机器学习分类问题共三类\n",
        "    NClass = 3\n",
        "    #每类的训练序列个数\n",
        "    NTrain = 1000\n",
        "    #导入训练集\n",
        "    train_data = np.loadtxt('./train_modulation.csv', delimiter = ',', dtype = float)\n",
        "    #输出正例\n",
        "    ydata = train_data[:,-1]\n",
        "    #输入的高阶累积量\n",
        "    xdata = np.delete(train_data, -1, axis=1)\n",
        "    index = np.arange(ydata.shape[0])\n",
        "    #打乱数据集的顺序\n",
        "    np.random.shuffle(index)\n",
        "    xdata = xdata[index,:]\n",
        "    ydata = ydata[index]\n",
        "    #把输出类型变换为one hot向量 注意最终模型输出维度为3，输出的结果为预测为8PSK 4PSK 16Qam的每一种类型的概率大小，概率最大的那个就是我们最终调制识别的类型\n",
        "    ydata = np_utils.to_categorical(ydata, NClass)\n",
        "    print(int(xdata.shape[0]*9/10))\n",
        "    #分割训练集和验证集\n",
        "    xTrain = xdata[0:int(xdata.shape[0]*9/10),:]\n",
        "    yTrain = ydata[0:int(xdata.shape[0]*9/10),:]\n",
        "    #此为验证集\n",
        "    xVal = xdata[int(xdata.shape[0]*9/10):-1,:]\n",
        "    yVal = ydata[int(xdata.shape[0]*9/10):-1,:]\n",
        "    \n",
        "    # train model\n",
        "    _in_ = Input(shape = (xTrain.shape[1],))\n",
        "    ot = Dense(512,activation='relu')(_in_)\n",
        "    ot = Dense(64,use_bias=True, activation='relu')(ot)\n",
        "    ot = Dense(9,use_bias=True, activation='relu')(ot)\n",
        "    #ot = Dense(64,use_bias=True, activation='relu')(ot)\n",
        "    #ot = Dense(32,use_bias=True, activation='relu')(ot)\n",
        "    #ot = Dense(16,use_bias=True, activation='relu')(ot)\n",
        "    #ot = Dense(8,use_bias=True, activation='relu')(ot)\n",
        "    #ot = Dense(10,use_bias=True, activation='relu')(ot)\n",
        "    _out_ = Dense(NClass, activation='softmax')(ot)\n",
        "    model = Model(_in_, _out_)\n",
        "\n",
        "\n",
        "    #early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
        "    checkpoint = ModelCheckpoint(filepath='weights', monitor='val_loss', verbose=1, save_best_only=False)\n",
        "    adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n",
        "    model.compile(loss='categorical_crossentropy',\n",
        "                   optimizer=adam,\n",
        "                   metrics=['categorical_accuracy'])\n",
        "\n",
        "\n",
        "    model.fit(xTrain, yTrain, \n",
        "              epochs=2000, \n",
        "              batch_size=250,\n",
        "              validation_data=(xVal,yVal),\n",
        "              shuffle = True,\n",
        "              verbose = 2,\n",
        "              callbacks = [checkpoint])\n",
        "    print(\"evaluate the model - train_set:\")\n",
        "    model.summary()\n",
        "    \n",
        "  \n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001b[0m\n",
            "Epoch 756/2000\n",
            " - 0s - loss: 0.4308 - categorical_accuracy: 0.7607 - val_loss: 0.6978 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00756: saving model to weights\n",
            "Epoch 757/2000\n",
            " - 0s - loss: 0.4281 - categorical_accuracy: 0.7604 - val_loss: 0.7147 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00757: saving model to weights\n",
            "Epoch 758/2000\n",
            " - 0s - loss: 0.4268 - categorical_accuracy: 0.7615 - val_loss: 0.7095 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00758: saving model to weights\n",
            "Epoch 759/2000\n",
            " - 0s - loss: 0.4264 - categorical_accuracy: 0.7630 - val_loss: 0.7122 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00759: saving model to weights\n",
            "Epoch 760/2000\n",
            " - 0s - loss: 0.4259 - categorical_accuracy: 0.7630 - val_loss: 0.7275 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00760: saving model to weights\n",
            "Epoch 761/2000\n",
            " - 0s - loss: 0.4251 - categorical_accuracy: 0.7648 - val_loss: 0.7197 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00761: saving model to weights\n",
            "Epoch 762/2000\n",
            " - 0s - loss: 0.4247 - categorical_accuracy: 0.7622 - val_loss: 0.7079 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00762: saving model to weights\n",
            "Epoch 763/2000\n",
            " - 0s - loss: 0.4266 - categorical_accuracy: 0.7600 - val_loss: 0.6912 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00763: saving model to weights\n",
            "Epoch 764/2000\n",
            " - 0s - loss: 0.4298 - categorical_accuracy: 0.7607 - val_loss: 0.7096 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00764: saving model to weights\n",
            "Epoch 765/2000\n",
            " - 0s - loss: 0.4252 - categorical_accuracy: 0.7600 - val_loss: 0.7247 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00765: saving model to weights\n",
            "Epoch 766/2000\n",
            " - 0s - loss: 0.4231 - categorical_accuracy: 0.7674 - val_loss: 0.6971 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00766: saving model to weights\n",
            "Epoch 767/2000\n",
            " - 0s - loss: 0.4221 - categorical_accuracy: 0.7711 - val_loss: 0.7050 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00767: saving model to weights\n",
            "Epoch 768/2000\n",
            " - 0s - loss: 0.4224 - categorical_accuracy: 0.7700 - val_loss: 0.7054 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00768: saving model to weights\n",
            "Epoch 769/2000\n",
            " - 0s - loss: 0.4256 - categorical_accuracy: 0.7693 - val_loss: 0.7007 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00769: saving model to weights\n",
            "Epoch 770/2000\n",
            " - 0s - loss: 0.4258 - categorical_accuracy: 0.7670 - val_loss: 0.7014 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00770: saving model to weights\n",
            "Epoch 771/2000\n",
            " - 0s - loss: 0.4249 - categorical_accuracy: 0.7667 - val_loss: 0.7093 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00771: saving model to weights\n",
            "Epoch 772/2000\n",
            " - 0s - loss: 0.4255 - categorical_accuracy: 0.7681 - val_loss: 0.7139 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00772: saving model to weights\n",
            "Epoch 773/2000\n",
            " - 0s - loss: 0.4225 - categorical_accuracy: 0.7696 - val_loss: 0.7325 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00773: saving model to weights\n",
            "Epoch 774/2000\n",
            " - 0s - loss: 0.4227 - categorical_accuracy: 0.7681 - val_loss: 0.7518 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 00774: saving model to weights\n",
            "Epoch 775/2000\n",
            " - 0s - loss: 0.4255 - categorical_accuracy: 0.7678 - val_loss: 0.7078 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00775: saving model to weights\n",
            "Epoch 776/2000\n",
            " - 0s - loss: 0.4306 - categorical_accuracy: 0.7700 - val_loss: 0.7341 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 00776: saving model to weights\n",
            "Epoch 777/2000\n",
            " - 0s - loss: 0.4514 - categorical_accuracy: 0.7511 - val_loss: 0.7562 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00777: saving model to weights\n",
            "Epoch 778/2000\n",
            " - 0s - loss: 0.4765 - categorical_accuracy: 0.7500 - val_loss: 0.8073 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00778: saving model to weights\n",
            "Epoch 779/2000\n",
            " - 0s - loss: 0.5289 - categorical_accuracy: 0.7393 - val_loss: 0.8621 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 00779: saving model to weights\n",
            "Epoch 780/2000\n",
            " - 0s - loss: 0.9163 - categorical_accuracy: 0.7030 - val_loss: 1.8750 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 00780: saving model to weights\n",
            "Epoch 781/2000\n",
            " - 0s - loss: 0.8122 - categorical_accuracy: 0.6963 - val_loss: 0.8699 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 00781: saving model to weights\n",
            "Epoch 782/2000\n",
            " - 0s - loss: 0.5805 - categorical_accuracy: 0.7096 - val_loss: 0.7552 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00782: saving model to weights\n",
            "Epoch 783/2000\n",
            " - 0s - loss: 0.5075 - categorical_accuracy: 0.7315 - val_loss: 0.7127 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00783: saving model to weights\n",
            "Epoch 784/2000\n",
            " - 0s - loss: 0.4595 - categorical_accuracy: 0.7444 - val_loss: 0.6903 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00784: saving model to weights\n",
            "Epoch 785/2000\n",
            " - 0s - loss: 0.4549 - categorical_accuracy: 0.7478 - val_loss: 0.6784 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00785: saving model to weights\n",
            "Epoch 786/2000\n",
            " - 0s - loss: 0.4667 - categorical_accuracy: 0.7444 - val_loss: 0.8871 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 00786: saving model to weights\n",
            "Epoch 787/2000\n",
            " - 0s - loss: 0.5745 - categorical_accuracy: 0.7230 - val_loss: 0.9163 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 00787: saving model to weights\n",
            "Epoch 788/2000\n",
            " - 0s - loss: 0.5107 - categorical_accuracy: 0.7311 - val_loss: 0.7934 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00788: saving model to weights\n",
            "Epoch 789/2000\n",
            " - 0s - loss: 0.5065 - categorical_accuracy: 0.7344 - val_loss: 0.7564 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00789: saving model to weights\n",
            "Epoch 790/2000\n",
            " - 0s - loss: 0.4853 - categorical_accuracy: 0.7419 - val_loss: 0.7440 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00790: saving model to weights\n",
            "Epoch 791/2000\n",
            " - 0s - loss: 0.4655 - categorical_accuracy: 0.7456 - val_loss: 0.8012 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00791: saving model to weights\n",
            "Epoch 792/2000\n",
            " - 0s - loss: 0.4812 - categorical_accuracy: 0.7467 - val_loss: 1.1398 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 00792: saving model to weights\n",
            "Epoch 793/2000\n",
            " - 0s - loss: 0.5329 - categorical_accuracy: 0.7359 - val_loss: 0.7557 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00793: saving model to weights\n",
            "Epoch 794/2000\n",
            " - 0s - loss: 0.4470 - categorical_accuracy: 0.7533 - val_loss: 0.7202 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00794: saving model to weights\n",
            "Epoch 795/2000\n",
            " - 0s - loss: 0.4438 - categorical_accuracy: 0.7541 - val_loss: 0.7375 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00795: saving model to weights\n",
            "Epoch 796/2000\n",
            " - 0s - loss: 0.4422 - categorical_accuracy: 0.7507 - val_loss: 0.7499 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00796: saving model to weights\n",
            "Epoch 797/2000\n",
            " - 0s - loss: 0.4391 - categorical_accuracy: 0.7526 - val_loss: 0.7136 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00797: saving model to weights\n",
            "Epoch 798/2000\n",
            " - 0s - loss: 0.4360 - categorical_accuracy: 0.7559 - val_loss: 0.7675 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00798: saving model to weights\n",
            "Epoch 799/2000\n",
            " - 0s - loss: 0.4325 - categorical_accuracy: 0.7548 - val_loss: 0.7498 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00799: saving model to weights\n",
            "Epoch 800/2000\n",
            " - 0s - loss: 0.4306 - categorical_accuracy: 0.7567 - val_loss: 0.7681 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00800: saving model to weights\n",
            "Epoch 801/2000\n",
            " - 0s - loss: 0.4286 - categorical_accuracy: 0.7570 - val_loss: 0.7742 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00801: saving model to weights\n",
            "Epoch 802/2000\n",
            " - 0s - loss: 0.4282 - categorical_accuracy: 0.7593 - val_loss: 0.7562 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00802: saving model to weights\n",
            "Epoch 803/2000\n",
            " - 0s - loss: 0.4290 - categorical_accuracy: 0.7548 - val_loss: 0.7574 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00803: saving model to weights\n",
            "Epoch 804/2000\n",
            " - 0s - loss: 0.4271 - categorical_accuracy: 0.7585 - val_loss: 0.7533 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00804: saving model to weights\n",
            "Epoch 805/2000\n",
            " - 0s - loss: 0.4249 - categorical_accuracy: 0.7581 - val_loss: 0.7978 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00805: saving model to weights\n",
            "Epoch 806/2000\n",
            " - 0s - loss: 0.4254 - categorical_accuracy: 0.7581 - val_loss: 0.8517 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00806: saving model to weights\n",
            "Epoch 807/2000\n",
            " - 0s - loss: 0.4290 - categorical_accuracy: 0.7585 - val_loss: 0.7213 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00807: saving model to weights\n",
            "Epoch 808/2000\n",
            " - 0s - loss: 0.4288 - categorical_accuracy: 0.7552 - val_loss: 0.6912 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00808: saving model to weights\n",
            "Epoch 809/2000\n",
            " - 0s - loss: 0.4274 - categorical_accuracy: 0.7581 - val_loss: 0.6968 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00809: saving model to weights\n",
            "Epoch 810/2000\n",
            " - 0s - loss: 0.4253 - categorical_accuracy: 0.7600 - val_loss: 0.7124 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00810: saving model to weights\n",
            "Epoch 811/2000\n",
            " - 0s - loss: 0.4252 - categorical_accuracy: 0.7593 - val_loss: 0.7010 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00811: saving model to weights\n",
            "Epoch 812/2000\n",
            " - 0s - loss: 0.4250 - categorical_accuracy: 0.7633 - val_loss: 0.7412 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00812: saving model to weights\n",
            "Epoch 813/2000\n",
            " - 0s - loss: 0.4235 - categorical_accuracy: 0.7604 - val_loss: 0.7313 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00813: saving model to weights\n",
            "Epoch 814/2000\n",
            " - 0s - loss: 0.4218 - categorical_accuracy: 0.7619 - val_loss: 0.7431 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00814: saving model to weights\n",
            "Epoch 815/2000\n",
            " - 0s - loss: 0.4219 - categorical_accuracy: 0.7630 - val_loss: 0.7203 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00815: saving model to weights\n",
            "Epoch 816/2000\n",
            " - 0s - loss: 0.4203 - categorical_accuracy: 0.7652 - val_loss: 0.7388 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00816: saving model to weights\n",
            "Epoch 817/2000\n",
            " - 0s - loss: 0.4215 - categorical_accuracy: 0.7615 - val_loss: 0.7436 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00817: saving model to weights\n",
            "Epoch 818/2000\n",
            " - 0s - loss: 0.4197 - categorical_accuracy: 0.7626 - val_loss: 0.7672 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00818: saving model to weights\n",
            "Epoch 819/2000\n",
            " - 0s - loss: 0.4649 - categorical_accuracy: 0.7441 - val_loss: 0.7391 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00819: saving model to weights\n",
            "Epoch 820/2000\n",
            " - 0s - loss: 0.5099 - categorical_accuracy: 0.7278 - val_loss: 0.7689 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00820: saving model to weights\n",
            "Epoch 821/2000\n",
            " - 0s - loss: 0.5053 - categorical_accuracy: 0.7230 - val_loss: 0.7938 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00821: saving model to weights\n",
            "Epoch 822/2000\n",
            " - 0s - loss: 0.4845 - categorical_accuracy: 0.7259 - val_loss: 0.7331 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00822: saving model to weights\n",
            "Epoch 823/2000\n",
            " - 0s - loss: 0.4830 - categorical_accuracy: 0.7267 - val_loss: 0.8332 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00823: saving model to weights\n",
            "Epoch 824/2000\n",
            " - 0s - loss: 0.4849 - categorical_accuracy: 0.7267 - val_loss: 0.7413 - val_categorical_accuracy: 0.7258\n",
            "\n",
            "Epoch 00824: saving model to weights\n",
            "Epoch 825/2000\n",
            " - 0s - loss: 0.4639 - categorical_accuracy: 0.7337 - val_loss: 0.7154 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00825: saving model to weights\n",
            "Epoch 826/2000\n",
            " - 0s - loss: 0.4587 - categorical_accuracy: 0.7404 - val_loss: 0.7440 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00826: saving model to weights\n",
            "Epoch 827/2000\n",
            " - 0s - loss: 0.4546 - categorical_accuracy: 0.7393 - val_loss: 0.7236 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00827: saving model to weights\n",
            "Epoch 828/2000\n",
            " - 0s - loss: 0.4561 - categorical_accuracy: 0.7407 - val_loss: 0.7064 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00828: saving model to weights\n",
            "Epoch 829/2000\n",
            " - 0s - loss: 0.4527 - categorical_accuracy: 0.7437 - val_loss: 0.7081 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00829: saving model to weights\n",
            "Epoch 830/2000\n",
            " - 0s - loss: 0.4503 - categorical_accuracy: 0.7456 - val_loss: 0.7272 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00830: saving model to weights\n",
            "Epoch 831/2000\n",
            " - 0s - loss: 0.4479 - categorical_accuracy: 0.7430 - val_loss: 0.7078 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00831: saving model to weights\n",
            "Epoch 832/2000\n",
            " - 0s - loss: 0.4465 - categorical_accuracy: 0.7470 - val_loss: 0.7167 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00832: saving model to weights\n",
            "Epoch 833/2000\n",
            " - 0s - loss: 0.4463 - categorical_accuracy: 0.7463 - val_loss: 0.7031 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00833: saving model to weights\n",
            "Epoch 834/2000\n",
            " - 0s - loss: 0.4477 - categorical_accuracy: 0.7448 - val_loss: 0.7159 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00834: saving model to weights\n",
            "Epoch 835/2000\n",
            " - 0s - loss: 0.4469 - categorical_accuracy: 0.7463 - val_loss: 0.7305 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00835: saving model to weights\n",
            "Epoch 836/2000\n",
            " - 0s - loss: 0.4447 - categorical_accuracy: 0.7463 - val_loss: 0.7043 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00836: saving model to weights\n",
            "Epoch 837/2000\n",
            " - 0s - loss: 0.4452 - categorical_accuracy: 0.7481 - val_loss: 0.7064 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00837: saving model to weights\n",
            "Epoch 838/2000\n",
            " - 0s - loss: 0.4419 - categorical_accuracy: 0.7504 - val_loss: 0.7397 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00838: saving model to weights\n",
            "Epoch 839/2000\n",
            " - 0s - loss: 0.4427 - categorical_accuracy: 0.7507 - val_loss: 0.7611 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00839: saving model to weights\n",
            "Epoch 840/2000\n",
            " - 0s - loss: 0.4453 - categorical_accuracy: 0.7485 - val_loss: 0.7237 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00840: saving model to weights\n",
            "Epoch 841/2000\n",
            " - 0s - loss: 0.4429 - categorical_accuracy: 0.7489 - val_loss: 0.7423 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00841: saving model to weights\n",
            "Epoch 842/2000\n",
            " - 0s - loss: 0.4400 - categorical_accuracy: 0.7500 - val_loss: 0.7196 - val_categorical_accuracy: 0.7258\n",
            "\n",
            "Epoch 00842: saving model to weights\n",
            "Epoch 843/2000\n",
            " - 0s - loss: 0.4397 - categorical_accuracy: 0.7522 - val_loss: 0.7252 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00843: saving model to weights\n",
            "Epoch 844/2000\n",
            " - 0s - loss: 0.4381 - categorical_accuracy: 0.7515 - val_loss: 0.7346 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00844: saving model to weights\n",
            "Epoch 845/2000\n",
            " - 0s - loss: 0.4383 - categorical_accuracy: 0.7493 - val_loss: 0.6968 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00845: saving model to weights\n",
            "Epoch 846/2000\n",
            " - 0s - loss: 0.4387 - categorical_accuracy: 0.7548 - val_loss: 0.7319 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00846: saving model to weights\n",
            "Epoch 847/2000\n",
            " - 0s - loss: 0.4374 - categorical_accuracy: 0.7530 - val_loss: 0.7065 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00847: saving model to weights\n",
            "Epoch 848/2000\n",
            " - 0s - loss: 0.4362 - categorical_accuracy: 0.7519 - val_loss: 0.7225 - val_categorical_accuracy: 0.7258\n",
            "\n",
            "Epoch 00848: saving model to weights\n",
            "Epoch 849/2000\n",
            " - 0s - loss: 0.4361 - categorical_accuracy: 0.7496 - val_loss: 0.7211 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00849: saving model to weights\n",
            "Epoch 850/2000\n",
            " - 0s - loss: 0.4356 - categorical_accuracy: 0.7522 - val_loss: 0.7191 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00850: saving model to weights\n",
            "Epoch 851/2000\n",
            " - 0s - loss: 0.4345 - categorical_accuracy: 0.7537 - val_loss: 0.7053 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00851: saving model to weights\n",
            "Epoch 852/2000\n",
            " - 0s - loss: 0.4360 - categorical_accuracy: 0.7533 - val_loss: 0.7289 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00852: saving model to weights\n",
            "Epoch 853/2000\n",
            " - 0s - loss: 0.4358 - categorical_accuracy: 0.7533 - val_loss: 0.7097 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00853: saving model to weights\n",
            "Epoch 854/2000\n",
            " - 0s - loss: 0.4341 - categorical_accuracy: 0.7541 - val_loss: 0.7030 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00854: saving model to weights\n",
            "Epoch 855/2000\n",
            " - 0s - loss: 0.4341 - categorical_accuracy: 0.7519 - val_loss: 0.7169 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00855: saving model to weights\n",
            "Epoch 856/2000\n",
            " - 0s - loss: 0.4330 - categorical_accuracy: 0.7533 - val_loss: 0.7268 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00856: saving model to weights\n",
            "Epoch 857/2000\n",
            " - 0s - loss: 0.4325 - categorical_accuracy: 0.7537 - val_loss: 0.6950 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00857: saving model to weights\n",
            "Epoch 858/2000\n",
            " - 0s - loss: 0.4308 - categorical_accuracy: 0.7556 - val_loss: 0.7320 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00858: saving model to weights\n",
            "Epoch 859/2000\n",
            " - 0s - loss: 0.4323 - categorical_accuracy: 0.7548 - val_loss: 0.6968 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00859: saving model to weights\n",
            "Epoch 860/2000\n",
            " - 0s - loss: 0.4307 - categorical_accuracy: 0.7541 - val_loss: 0.7139 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00860: saving model to weights\n",
            "Epoch 861/2000\n",
            " - 0s - loss: 0.4319 - categorical_accuracy: 0.7541 - val_loss: 0.6987 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00861: saving model to weights\n",
            "Epoch 862/2000\n",
            " - 0s - loss: 0.4316 - categorical_accuracy: 0.7548 - val_loss: 0.7110 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00862: saving model to weights\n",
            "Epoch 863/2000\n",
            " - 0s - loss: 0.4313 - categorical_accuracy: 0.7522 - val_loss: 0.6873 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00863: saving model to weights\n",
            "Epoch 864/2000\n",
            " - 0s - loss: 0.4304 - categorical_accuracy: 0.7548 - val_loss: 0.7299 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00864: saving model to weights\n",
            "Epoch 865/2000\n",
            " - 0s - loss: 0.4339 - categorical_accuracy: 0.7511 - val_loss: 0.7163 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00865: saving model to weights\n",
            "Epoch 866/2000\n",
            " - 0s - loss: 0.4318 - categorical_accuracy: 0.7537 - val_loss: 0.7136 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00866: saving model to weights\n",
            "Epoch 867/2000\n",
            " - 0s - loss: 0.4326 - categorical_accuracy: 0.7570 - val_loss: 0.7289 - val_categorical_accuracy: 0.7258\n",
            "\n",
            "Epoch 00867: saving model to weights\n",
            "Epoch 868/2000\n",
            " - 0s - loss: 0.4288 - categorical_accuracy: 0.7526 - val_loss: 0.7241 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00868: saving model to weights\n",
            "Epoch 869/2000\n",
            " - 0s - loss: 0.4293 - categorical_accuracy: 0.7548 - val_loss: 0.7232 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00869: saving model to weights\n",
            "Epoch 870/2000\n",
            " - 0s - loss: 0.4286 - categorical_accuracy: 0.7570 - val_loss: 0.7208 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00870: saving model to weights\n",
            "Epoch 871/2000\n",
            " - 0s - loss: 0.4261 - categorical_accuracy: 0.7578 - val_loss: 0.7131 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00871: saving model to weights\n",
            "Epoch 872/2000\n",
            " - 0s - loss: 0.4263 - categorical_accuracy: 0.7578 - val_loss: 0.7012 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00872: saving model to weights\n",
            "Epoch 873/2000\n",
            " - 0s - loss: 0.4261 - categorical_accuracy: 0.7574 - val_loss: 0.7217 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00873: saving model to weights\n",
            "Epoch 874/2000\n",
            " - 0s - loss: 0.4253 - categorical_accuracy: 0.7559 - val_loss: 0.7126 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00874: saving model to weights\n",
            "Epoch 875/2000\n",
            " - 0s - loss: 0.4287 - categorical_accuracy: 0.7567 - val_loss: 0.7047 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00875: saving model to weights\n",
            "Epoch 876/2000\n",
            " - 0s - loss: 0.4255 - categorical_accuracy: 0.7548 - val_loss: 0.7228 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00876: saving model to weights\n",
            "Epoch 877/2000\n",
            " - 0s - loss: 0.4250 - categorical_accuracy: 0.7574 - val_loss: 0.7142 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00877: saving model to weights\n",
            "Epoch 878/2000\n",
            " - 0s - loss: 0.4268 - categorical_accuracy: 0.7559 - val_loss: 0.7209 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00878: saving model to weights\n",
            "Epoch 879/2000\n",
            " - 0s - loss: 0.4238 - categorical_accuracy: 0.7544 - val_loss: 0.7496 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00879: saving model to weights\n",
            "Epoch 880/2000\n",
            " - 0s - loss: 0.4236 - categorical_accuracy: 0.7585 - val_loss: 0.7096 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00880: saving model to weights\n",
            "Epoch 881/2000\n",
            " - 0s - loss: 0.4292 - categorical_accuracy: 0.7556 - val_loss: 0.7389 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00881: saving model to weights\n",
            "Epoch 882/2000\n",
            " - 0s - loss: 0.4298 - categorical_accuracy: 0.7559 - val_loss: 0.7233 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00882: saving model to weights\n",
            "Epoch 883/2000\n",
            " - 0s - loss: 0.4273 - categorical_accuracy: 0.7537 - val_loss: 0.7581 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00883: saving model to weights\n",
            "Epoch 884/2000\n",
            " - 0s - loss: 0.7116 - categorical_accuracy: 0.7285 - val_loss: 0.8271 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00884: saving model to weights\n",
            "Epoch 885/2000\n",
            " - 0s - loss: 0.8211 - categorical_accuracy: 0.6933 - val_loss: 0.9910 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00885: saving model to weights\n",
            "Epoch 886/2000\n",
            " - 0s - loss: 0.5529 - categorical_accuracy: 0.7285 - val_loss: 0.7253 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00886: saving model to weights\n",
            "Epoch 887/2000\n",
            " - 0s - loss: 0.5319 - categorical_accuracy: 0.7278 - val_loss: 0.7881 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00887: saving model to weights\n",
            "Epoch 888/2000\n",
            " - 0s - loss: 0.5068 - categorical_accuracy: 0.7352 - val_loss: 0.7864 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00888: saving model to weights\n",
            "Epoch 889/2000\n",
            " - 0s - loss: 0.4821 - categorical_accuracy: 0.7452 - val_loss: 0.8396 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00889: saving model to weights\n",
            "Epoch 890/2000\n",
            " - 0s - loss: 0.4718 - categorical_accuracy: 0.7470 - val_loss: 0.7313 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00890: saving model to weights\n",
            "Epoch 891/2000\n",
            " - 0s - loss: 0.4622 - categorical_accuracy: 0.7507 - val_loss: 0.7526 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00891: saving model to weights\n",
            "Epoch 892/2000\n",
            " - 0s - loss: 0.4544 - categorical_accuracy: 0.7485 - val_loss: 0.7315 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00892: saving model to weights\n",
            "Epoch 893/2000\n",
            " - 0s - loss: 0.4507 - categorical_accuracy: 0.7541 - val_loss: 0.7350 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00893: saving model to weights\n",
            "Epoch 894/2000\n",
            " - 0s - loss: 0.4490 - categorical_accuracy: 0.7537 - val_loss: 0.7109 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00894: saving model to weights\n",
            "Epoch 895/2000\n",
            " - 0s - loss: 0.4477 - categorical_accuracy: 0.7526 - val_loss: 0.7340 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00895: saving model to weights\n",
            "Epoch 896/2000\n",
            " - 0s - loss: 0.4495 - categorical_accuracy: 0.7552 - val_loss: 0.7596 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00896: saving model to weights\n",
            "Epoch 897/2000\n",
            " - 0s - loss: 0.4440 - categorical_accuracy: 0.7563 - val_loss: 0.7860 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00897: saving model to weights\n",
            "Epoch 898/2000\n",
            " - 0s - loss: 0.4409 - categorical_accuracy: 0.7567 - val_loss: 0.8146 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00898: saving model to weights\n",
            "Epoch 899/2000\n",
            " - 0s - loss: 0.4388 - categorical_accuracy: 0.7548 - val_loss: 0.7642 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00899: saving model to weights\n",
            "Epoch 900/2000\n",
            " - 0s - loss: 0.4359 - categorical_accuracy: 0.7574 - val_loss: 0.8065 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00900: saving model to weights\n",
            "Epoch 901/2000\n",
            " - 0s - loss: 0.4346 - categorical_accuracy: 0.7556 - val_loss: 0.7878 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00901: saving model to weights\n",
            "Epoch 902/2000\n",
            " - 0s - loss: 0.4299 - categorical_accuracy: 0.7570 - val_loss: 0.8273 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00902: saving model to weights\n",
            "Epoch 903/2000\n",
            " - 0s - loss: 0.4299 - categorical_accuracy: 0.7570 - val_loss: 0.8286 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00903: saving model to weights\n",
            "Epoch 904/2000\n",
            " - 0s - loss: 0.4283 - categorical_accuracy: 0.7596 - val_loss: 0.8210 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00904: saving model to weights\n",
            "Epoch 905/2000\n",
            " - 0s - loss: 0.4279 - categorical_accuracy: 0.7581 - val_loss: 0.7948 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00905: saving model to weights\n",
            "Epoch 906/2000\n",
            " - 0s - loss: 0.4264 - categorical_accuracy: 0.7615 - val_loss: 0.8252 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00906: saving model to weights\n",
            "Epoch 907/2000\n",
            " - 0s - loss: 0.4255 - categorical_accuracy: 0.7581 - val_loss: 0.8598 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00907: saving model to weights\n",
            "Epoch 908/2000\n",
            " - 0s - loss: 0.4277 - categorical_accuracy: 0.7567 - val_loss: 0.8848 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00908: saving model to weights\n",
            "Epoch 909/2000\n",
            " - 0s - loss: 0.4292 - categorical_accuracy: 0.7552 - val_loss: 0.8825 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00909: saving model to weights\n",
            "Epoch 910/2000\n",
            " - 0s - loss: 0.4291 - categorical_accuracy: 0.7578 - val_loss: 0.8889 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00910: saving model to weights\n",
            "Epoch 911/2000\n",
            " - 0s - loss: 0.4265 - categorical_accuracy: 0.7567 - val_loss: 0.8693 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00911: saving model to weights\n",
            "Epoch 912/2000\n",
            " - 0s - loss: 0.4223 - categorical_accuracy: 0.7596 - val_loss: 0.8322 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00912: saving model to weights\n",
            "Epoch 913/2000\n",
            " - 0s - loss: 0.4219 - categorical_accuracy: 0.7596 - val_loss: 0.8565 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00913: saving model to weights\n",
            "Epoch 914/2000\n",
            " - 0s - loss: 0.4212 - categorical_accuracy: 0.7548 - val_loss: 0.8621 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00914: saving model to weights\n",
            "Epoch 915/2000\n",
            " - 0s - loss: 0.4180 - categorical_accuracy: 0.7630 - val_loss: 0.8962 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00915: saving model to weights\n",
            "Epoch 916/2000\n",
            " - 0s - loss: 0.4192 - categorical_accuracy: 0.7626 - val_loss: 0.8752 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00916: saving model to weights\n",
            "Epoch 917/2000\n",
            " - 0s - loss: 0.4202 - categorical_accuracy: 0.7622 - val_loss: 0.8610 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00917: saving model to weights\n",
            "Epoch 918/2000\n",
            " - 0s - loss: 0.4193 - categorical_accuracy: 0.7563 - val_loss: 0.8395 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00918: saving model to weights\n",
            "Epoch 919/2000\n",
            " - 0s - loss: 0.4202 - categorical_accuracy: 0.7622 - val_loss: 0.8314 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00919: saving model to weights\n",
            "Epoch 920/2000\n",
            " - 0s - loss: 0.4198 - categorical_accuracy: 0.7570 - val_loss: 0.8392 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00920: saving model to weights\n",
            "Epoch 921/2000\n",
            " - 0s - loss: 0.4163 - categorical_accuracy: 0.7615 - val_loss: 0.8601 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00921: saving model to weights\n",
            "Epoch 922/2000\n",
            " - 0s - loss: 0.4156 - categorical_accuracy: 0.7604 - val_loss: 0.8588 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00922: saving model to weights\n",
            "Epoch 923/2000\n",
            " - 0s - loss: 0.4156 - categorical_accuracy: 0.7615 - val_loss: 0.8529 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00923: saving model to weights\n",
            "Epoch 924/2000\n",
            " - 0s - loss: 0.4148 - categorical_accuracy: 0.7611 - val_loss: 0.8786 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00924: saving model to weights\n",
            "Epoch 925/2000\n",
            " - 0s - loss: 0.4138 - categorical_accuracy: 0.7604 - val_loss: 0.8838 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00925: saving model to weights\n",
            "Epoch 926/2000\n",
            " - 0s - loss: 0.4141 - categorical_accuracy: 0.7593 - val_loss: 0.8684 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00926: saving model to weights\n",
            "Epoch 927/2000\n",
            " - 0s - loss: 0.4142 - categorical_accuracy: 0.7604 - val_loss: 0.8762 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00927: saving model to weights\n",
            "Epoch 928/2000\n",
            " - 0s - loss: 0.4161 - categorical_accuracy: 0.7607 - val_loss: 0.9171 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00928: saving model to weights\n",
            "Epoch 929/2000\n",
            " - 0s - loss: 0.4146 - categorical_accuracy: 0.7611 - val_loss: 0.8628 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00929: saving model to weights\n",
            "Epoch 930/2000\n",
            " - 0s - loss: 0.4137 - categorical_accuracy: 0.7581 - val_loss: 0.8999 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00930: saving model to weights\n",
            "Epoch 931/2000\n",
            " - 0s - loss: 0.4169 - categorical_accuracy: 0.7596 - val_loss: 0.8728 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 00931: saving model to weights\n",
            "Epoch 932/2000\n",
            " - 0s - loss: 0.4163 - categorical_accuracy: 0.7593 - val_loss: 0.9087 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00932: saving model to weights\n",
            "Epoch 933/2000\n",
            " - 0s - loss: 0.4118 - categorical_accuracy: 0.7615 - val_loss: 0.9143 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00933: saving model to weights\n",
            "Epoch 934/2000\n",
            " - 0s - loss: 0.4106 - categorical_accuracy: 0.7589 - val_loss: 0.9327 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00934: saving model to weights\n",
            "Epoch 935/2000\n",
            " - 0s - loss: 0.4119 - categorical_accuracy: 0.7626 - val_loss: 0.8938 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00935: saving model to weights\n",
            "Epoch 936/2000\n",
            " - 0s - loss: 0.4127 - categorical_accuracy: 0.7593 - val_loss: 0.8970 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 00936: saving model to weights\n",
            "Epoch 937/2000\n",
            " - 0s - loss: 0.4101 - categorical_accuracy: 0.7593 - val_loss: 0.9673 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00937: saving model to weights\n",
            "Epoch 938/2000\n",
            " - 0s - loss: 0.4140 - categorical_accuracy: 0.7574 - val_loss: 0.9092 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00938: saving model to weights\n",
            "Epoch 939/2000\n",
            " - 0s - loss: 0.4125 - categorical_accuracy: 0.7581 - val_loss: 0.9615 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00939: saving model to weights\n",
            "Epoch 940/2000\n",
            " - 0s - loss: 0.4128 - categorical_accuracy: 0.7574 - val_loss: 0.9008 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00940: saving model to weights\n",
            "Epoch 941/2000\n",
            " - 0s - loss: 0.4100 - categorical_accuracy: 0.7604 - val_loss: 0.9666 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00941: saving model to weights\n",
            "Epoch 942/2000\n",
            " - 0s - loss: 0.4114 - categorical_accuracy: 0.7570 - val_loss: 0.9253 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00942: saving model to weights\n",
            "Epoch 943/2000\n",
            " - 0s - loss: 0.4115 - categorical_accuracy: 0.7604 - val_loss: 0.9235 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 00943: saving model to weights\n",
            "Epoch 944/2000\n",
            " - 0s - loss: 0.4100 - categorical_accuracy: 0.7700 - val_loss: 0.9547 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00944: saving model to weights\n",
            "Epoch 945/2000\n",
            " - 0s - loss: 0.4104 - categorical_accuracy: 0.7704 - val_loss: 1.0078 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00945: saving model to weights\n",
            "Epoch 946/2000\n",
            " - 0s - loss: 0.4117 - categorical_accuracy: 0.7711 - val_loss: 0.9936 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 00946: saving model to weights\n",
            "Epoch 947/2000\n",
            " - 0s - loss: 0.4135 - categorical_accuracy: 0.7681 - val_loss: 0.9540 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00947: saving model to weights\n",
            "Epoch 948/2000\n",
            " - 0s - loss: 0.4205 - categorical_accuracy: 0.7667 - val_loss: 0.8164 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00948: saving model to weights\n",
            "Epoch 949/2000\n",
            " - 0s - loss: 0.4170 - categorical_accuracy: 0.7678 - val_loss: 0.9277 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 00949: saving model to weights\n",
            "Epoch 950/2000\n",
            " - 0s - loss: 0.4088 - categorical_accuracy: 0.7696 - val_loss: 0.9678 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00950: saving model to weights\n",
            "Epoch 951/2000\n",
            " - 0s - loss: 0.4066 - categorical_accuracy: 0.7707 - val_loss: 0.9711 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00951: saving model to weights\n",
            "Epoch 952/2000\n",
            " - 0s - loss: 0.4066 - categorical_accuracy: 0.7711 - val_loss: 0.9616 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00952: saving model to weights\n",
            "Epoch 953/2000\n",
            " - 0s - loss: 0.4099 - categorical_accuracy: 0.7711 - val_loss: 1.0804 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 00953: saving model to weights\n",
            "Epoch 954/2000\n",
            " - 0s - loss: 0.4143 - categorical_accuracy: 0.7670 - val_loss: 0.9543 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 00954: saving model to weights\n",
            "Epoch 955/2000\n",
            " - 0s - loss: 0.4195 - categorical_accuracy: 0.7667 - val_loss: 1.0061 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 00955: saving model to weights\n",
            "Epoch 956/2000\n",
            " - 0s - loss: 0.4136 - categorical_accuracy: 0.7711 - val_loss: 0.9642 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00956: saving model to weights\n",
            "Epoch 957/2000\n",
            " - 0s - loss: 0.4096 - categorical_accuracy: 0.7685 - val_loss: 1.0299 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 00957: saving model to weights\n",
            "Epoch 958/2000\n",
            " - 0s - loss: 0.4053 - categorical_accuracy: 0.7722 - val_loss: 1.0054 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00958: saving model to weights\n",
            "Epoch 959/2000\n",
            " - 0s - loss: 0.4055 - categorical_accuracy: 0.7726 - val_loss: 1.0102 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00959: saving model to weights\n",
            "Epoch 960/2000\n",
            " - 0s - loss: 0.4044 - categorical_accuracy: 0.7733 - val_loss: 1.0119 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 00960: saving model to weights\n",
            "Epoch 961/2000\n",
            " - 0s - loss: 0.4059 - categorical_accuracy: 0.7767 - val_loss: 1.0252 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00961: saving model to weights\n",
            "Epoch 962/2000\n",
            " - 0s - loss: 0.4049 - categorical_accuracy: 0.7730 - val_loss: 1.0534 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 00962: saving model to weights\n",
            "Epoch 963/2000\n",
            " - 0s - loss: 0.4042 - categorical_accuracy: 0.7719 - val_loss: 1.0587 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00963: saving model to weights\n",
            "Epoch 964/2000\n",
            " - 0s - loss: 0.4040 - categorical_accuracy: 0.7730 - val_loss: 1.0271 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00964: saving model to weights\n",
            "Epoch 965/2000\n",
            " - 0s - loss: 0.4042 - categorical_accuracy: 0.7719 - val_loss: 1.0110 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 00965: saving model to weights\n",
            "Epoch 966/2000\n",
            " - 0s - loss: 0.4036 - categorical_accuracy: 0.7737 - val_loss: 1.0059 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00966: saving model to weights\n",
            "Epoch 967/2000\n",
            " - 0s - loss: 0.4034 - categorical_accuracy: 0.7719 - val_loss: 1.0164 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00967: saving model to weights\n",
            "Epoch 968/2000\n",
            " - 0s - loss: 0.4036 - categorical_accuracy: 0.7737 - val_loss: 0.9990 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00968: saving model to weights\n",
            "Epoch 969/2000\n",
            " - 0s - loss: 0.4010 - categorical_accuracy: 0.7719 - val_loss: 1.0173 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00969: saving model to weights\n",
            "Epoch 970/2000\n",
            " - 0s - loss: 0.4008 - categorical_accuracy: 0.7744 - val_loss: 0.9982 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00970: saving model to weights\n",
            "Epoch 971/2000\n",
            " - 0s - loss: 0.4019 - categorical_accuracy: 0.7737 - val_loss: 1.0407 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00971: saving model to weights\n",
            "Epoch 972/2000\n",
            " - 0s - loss: 0.3993 - categorical_accuracy: 0.7737 - val_loss: 1.0899 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 00972: saving model to weights\n",
            "Epoch 973/2000\n",
            " - 0s - loss: 0.4045 - categorical_accuracy: 0.7759 - val_loss: 1.1077 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 00973: saving model to weights\n",
            "Epoch 974/2000\n",
            " - 0s - loss: 0.4364 - categorical_accuracy: 0.7652 - val_loss: 0.9342 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 00974: saving model to weights\n",
            "Epoch 975/2000\n",
            " - 0s - loss: 0.4369 - categorical_accuracy: 0.7507 - val_loss: 0.8852 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 00975: saving model to weights\n",
            "Epoch 976/2000\n",
            " - 0s - loss: 0.4425 - categorical_accuracy: 0.7478 - val_loss: 0.8329 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 00976: saving model to weights\n",
            "Epoch 977/2000\n",
            " - 0s - loss: 0.4351 - categorical_accuracy: 0.7504 - val_loss: 0.9083 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 00977: saving model to weights\n",
            "Epoch 978/2000\n",
            " - 0s - loss: 0.4306 - categorical_accuracy: 0.7615 - val_loss: 0.9004 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00978: saving model to weights\n",
            "Epoch 979/2000\n",
            " - 0s - loss: 0.4264 - categorical_accuracy: 0.7593 - val_loss: 0.8856 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00979: saving model to weights\n",
            "Epoch 980/2000\n",
            " - 0s - loss: 0.4242 - categorical_accuracy: 0.7644 - val_loss: 0.9075 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00980: saving model to weights\n",
            "Epoch 981/2000\n",
            " - 0s - loss: 0.4217 - categorical_accuracy: 0.7615 - val_loss: 0.9677 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00981: saving model to weights\n",
            "Epoch 982/2000\n",
            " - 0s - loss: 0.4172 - categorical_accuracy: 0.7644 - val_loss: 0.9265 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00982: saving model to weights\n",
            "Epoch 983/2000\n",
            " - 0s - loss: 0.4151 - categorical_accuracy: 0.7600 - val_loss: 0.9575 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 00983: saving model to weights\n",
            "Epoch 984/2000\n",
            " - 0s - loss: 0.5223 - categorical_accuracy: 0.7515 - val_loss: 1.1620 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 00984: saving model to weights\n",
            "Epoch 985/2000\n",
            " - 0s - loss: 0.6392 - categorical_accuracy: 0.7311 - val_loss: 1.7043 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 00985: saving model to weights\n",
            "Epoch 986/2000\n",
            " - 0s - loss: 0.5629 - categorical_accuracy: 0.7337 - val_loss: 0.9667 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 00986: saving model to weights\n",
            "Epoch 987/2000\n",
            " - 0s - loss: 0.4305 - categorical_accuracy: 0.7530 - val_loss: 0.9267 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00987: saving model to weights\n",
            "Epoch 988/2000\n",
            " - 0s - loss: 0.4223 - categorical_accuracy: 0.7556 - val_loss: 0.9776 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00988: saving model to weights\n",
            "Epoch 989/2000\n",
            " - 0s - loss: 0.5081 - categorical_accuracy: 0.7415 - val_loss: 1.3539 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 00989: saving model to weights\n",
            "Epoch 990/2000\n",
            " - 0s - loss: 0.4971 - categorical_accuracy: 0.7437 - val_loss: 1.0650 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00990: saving model to weights\n",
            "Epoch 991/2000\n",
            " - 0s - loss: 0.4502 - categorical_accuracy: 0.7567 - val_loss: 0.8886 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00991: saving model to weights\n",
            "Epoch 992/2000\n",
            " - 0s - loss: 0.4541 - categorical_accuracy: 0.7478 - val_loss: 0.9477 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00992: saving model to weights\n",
            "Epoch 993/2000\n",
            " - 0s - loss: 0.4235 - categorical_accuracy: 0.7593 - val_loss: 0.8741 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00993: saving model to weights\n",
            "Epoch 994/2000\n",
            " - 0s - loss: 0.4229 - categorical_accuracy: 0.7578 - val_loss: 0.9595 - val_categorical_accuracy: 0.7224\n",
            "\n",
            "Epoch 00994: saving model to weights\n",
            "Epoch 995/2000\n",
            " - 0s - loss: 0.4124 - categorical_accuracy: 0.7611 - val_loss: 0.9180 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00995: saving model to weights\n",
            "Epoch 996/2000\n",
            " - 0s - loss: 0.4122 - categorical_accuracy: 0.7633 - val_loss: 0.9465 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 00996: saving model to weights\n",
            "Epoch 997/2000\n",
            " - 0s - loss: 0.4084 - categorical_accuracy: 0.7626 - val_loss: 0.9891 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 00997: saving model to weights\n",
            "Epoch 998/2000\n",
            " - 0s - loss: 0.4109 - categorical_accuracy: 0.7604 - val_loss: 0.9841 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 00998: saving model to weights\n",
            "Epoch 999/2000\n",
            " - 0s - loss: 0.4086 - categorical_accuracy: 0.7630 - val_loss: 0.9298 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 00999: saving model to weights\n",
            "Epoch 1000/2000\n",
            " - 0s - loss: 0.4094 - categorical_accuracy: 0.7626 - val_loss: 0.9671 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 01000: saving model to weights\n",
            "Epoch 1001/2000\n",
            " - 0s - loss: 0.4042 - categorical_accuracy: 0.7741 - val_loss: 0.9575 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01001: saving model to weights\n",
            "Epoch 1002/2000\n",
            " - 0s - loss: 0.4042 - categorical_accuracy: 0.7707 - val_loss: 0.9524 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01002: saving model to weights\n",
            "Epoch 1003/2000\n",
            " - 0s - loss: 0.4059 - categorical_accuracy: 0.7689 - val_loss: 0.9908 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01003: saving model to weights\n",
            "Epoch 1004/2000\n",
            " - 0s - loss: 0.4047 - categorical_accuracy: 0.7704 - val_loss: 1.0292 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01004: saving model to weights\n",
            "Epoch 1005/2000\n",
            " - 0s - loss: 0.4039 - categorical_accuracy: 0.7711 - val_loss: 0.9636 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01005: saving model to weights\n",
            "Epoch 1006/2000\n",
            " - 0s - loss: 0.4027 - categorical_accuracy: 0.7707 - val_loss: 0.9818 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01006: saving model to weights\n",
            "Epoch 1007/2000\n",
            " - 0s - loss: 0.4031 - categorical_accuracy: 0.7678 - val_loss: 1.0308 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01007: saving model to weights\n",
            "Epoch 1008/2000\n",
            " - 0s - loss: 0.4019 - categorical_accuracy: 0.7715 - val_loss: 1.0487 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01008: saving model to weights\n",
            "Epoch 1009/2000\n",
            " - 0s - loss: 0.4009 - categorical_accuracy: 0.7730 - val_loss: 1.0282 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01009: saving model to weights\n",
            "Epoch 1010/2000\n",
            " - 0s - loss: 0.4009 - categorical_accuracy: 0.7733 - val_loss: 1.0672 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01010: saving model to weights\n",
            "Epoch 1011/2000\n",
            " - 0s - loss: 0.4005 - categorical_accuracy: 0.7719 - val_loss: 0.9936 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01011: saving model to weights\n",
            "Epoch 1012/2000\n",
            " - 0s - loss: 0.4045 - categorical_accuracy: 0.7741 - val_loss: 0.9044 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01012: saving model to weights\n",
            "Epoch 1013/2000\n",
            " - 0s - loss: 0.4024 - categorical_accuracy: 0.7700 - val_loss: 0.9431 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01013: saving model to weights\n",
            "Epoch 1014/2000\n",
            " - 0s - loss: 0.4003 - categorical_accuracy: 0.7722 - val_loss: 0.9607 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01014: saving model to weights\n",
            "Epoch 1015/2000\n",
            " - 0s - loss: 0.3989 - categorical_accuracy: 0.7748 - val_loss: 0.9293 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01015: saving model to weights\n",
            "Epoch 1016/2000\n",
            " - 0s - loss: 0.3989 - categorical_accuracy: 0.7733 - val_loss: 0.9655 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01016: saving model to weights\n",
            "Epoch 1017/2000\n",
            " - 0s - loss: 0.3986 - categorical_accuracy: 0.7748 - val_loss: 0.9939 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01017: saving model to weights\n",
            "Epoch 1018/2000\n",
            " - 0s - loss: 0.4013 - categorical_accuracy: 0.7722 - val_loss: 1.0212 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01018: saving model to weights\n",
            "Epoch 1019/2000\n",
            " - 0s - loss: 0.4025 - categorical_accuracy: 0.7737 - val_loss: 1.0379 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01019: saving model to weights\n",
            "Epoch 1020/2000\n",
            " - 0s - loss: 0.3974 - categorical_accuracy: 0.7730 - val_loss: 1.0549 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01020: saving model to weights\n",
            "Epoch 1021/2000\n",
            " - 0s - loss: 0.3995 - categorical_accuracy: 0.7748 - val_loss: 1.0770 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01021: saving model to weights\n",
            "Epoch 1022/2000\n",
            " - 0s - loss: 0.3978 - categorical_accuracy: 0.7770 - val_loss: 1.0630 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01022: saving model to weights\n",
            "Epoch 1023/2000\n",
            " - 0s - loss: 0.3992 - categorical_accuracy: 0.7730 - val_loss: 1.0706 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01023: saving model to weights\n",
            "Epoch 1024/2000\n",
            " - 0s - loss: 0.4024 - categorical_accuracy: 0.7704 - val_loss: 1.0709 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01024: saving model to weights\n",
            "Epoch 1025/2000\n",
            " - 0s - loss: 0.3992 - categorical_accuracy: 0.7726 - val_loss: 1.0081 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01025: saving model to weights\n",
            "Epoch 1026/2000\n",
            " - 0s - loss: 0.4000 - categorical_accuracy: 0.7741 - val_loss: 0.9914 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01026: saving model to weights\n",
            "Epoch 1027/2000\n",
            " - 0s - loss: 0.3998 - categorical_accuracy: 0.7726 - val_loss: 1.0247 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01027: saving model to weights\n",
            "Epoch 1028/2000\n",
            " - 0s - loss: 0.3956 - categorical_accuracy: 0.7744 - val_loss: 1.0948 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01028: saving model to weights\n",
            "Epoch 1029/2000\n",
            " - 0s - loss: 0.3990 - categorical_accuracy: 0.7696 - val_loss: 1.0475 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01029: saving model to weights\n",
            "Epoch 1030/2000\n",
            " - 0s - loss: 0.3945 - categorical_accuracy: 0.7756 - val_loss: 1.0632 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01030: saving model to weights\n",
            "Epoch 1031/2000\n",
            " - 0s - loss: 0.3945 - categorical_accuracy: 0.7767 - val_loss: 1.0105 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01031: saving model to weights\n",
            "Epoch 1032/2000\n",
            " - 0s - loss: 0.3947 - categorical_accuracy: 0.7770 - val_loss: 1.0384 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01032: saving model to weights\n",
            "Epoch 1033/2000\n",
            " - 0s - loss: 0.3935 - categorical_accuracy: 0.7737 - val_loss: 1.0523 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01033: saving model to weights\n",
            "Epoch 1034/2000\n",
            " - 0s - loss: 0.3925 - categorical_accuracy: 0.7752 - val_loss: 1.0397 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01034: saving model to weights\n",
            "Epoch 1035/2000\n",
            " - 0s - loss: 0.3939 - categorical_accuracy: 0.7774 - val_loss: 1.0927 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01035: saving model to weights\n",
            "Epoch 1036/2000\n",
            " - 0s - loss: 0.3961 - categorical_accuracy: 0.7715 - val_loss: 1.0726 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01036: saving model to weights\n",
            "Epoch 1037/2000\n",
            " - 0s - loss: 0.3999 - categorical_accuracy: 0.7696 - val_loss: 1.0436 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01037: saving model to weights\n",
            "Epoch 1038/2000\n",
            " - 0s - loss: 0.3954 - categorical_accuracy: 0.7733 - val_loss: 1.0593 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01038: saving model to weights\n",
            "Epoch 1039/2000\n",
            " - 0s - loss: 0.3973 - categorical_accuracy: 0.7741 - val_loss: 1.1143 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01039: saving model to weights\n",
            "Epoch 1040/2000\n",
            " - 0s - loss: 0.3942 - categorical_accuracy: 0.7752 - val_loss: 1.1032 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01040: saving model to weights\n",
            "Epoch 1041/2000\n",
            " - 0s - loss: 0.3929 - categorical_accuracy: 0.7793 - val_loss: 1.0380 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01041: saving model to weights\n",
            "Epoch 1042/2000\n",
            " - 0s - loss: 0.3975 - categorical_accuracy: 0.7707 - val_loss: 0.9963 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01042: saving model to weights\n",
            "Epoch 1043/2000\n",
            " - 0s - loss: 0.3967 - categorical_accuracy: 0.7700 - val_loss: 1.0557 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01043: saving model to weights\n",
            "Epoch 1044/2000\n",
            " - 0s - loss: 0.3945 - categorical_accuracy: 0.7737 - val_loss: 1.1147 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01044: saving model to weights\n",
            "Epoch 1045/2000\n",
            " - 0s - loss: 0.3937 - categorical_accuracy: 0.7741 - val_loss: 1.0556 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01045: saving model to weights\n",
            "Epoch 1046/2000\n",
            " - 0s - loss: 0.3922 - categorical_accuracy: 0.7741 - val_loss: 1.0761 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01046: saving model to weights\n",
            "Epoch 1047/2000\n",
            " - 0s - loss: 0.3946 - categorical_accuracy: 0.7741 - val_loss: 0.9009 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01047: saving model to weights\n",
            "Epoch 1048/2000\n",
            " - 0s - loss: 0.3939 - categorical_accuracy: 0.7752 - val_loss: 0.8992 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01048: saving model to weights\n",
            "Epoch 1049/2000\n",
            " - 0s - loss: 0.3935 - categorical_accuracy: 0.7726 - val_loss: 0.9270 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01049: saving model to weights\n",
            "Epoch 1050/2000\n",
            " - 0s - loss: 0.3930 - categorical_accuracy: 0.7759 - val_loss: 0.9106 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01050: saving model to weights\n",
            "Epoch 1051/2000\n",
            " - 0s - loss: 0.3926 - categorical_accuracy: 0.7774 - val_loss: 0.9649 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01051: saving model to weights\n",
            "Epoch 1052/2000\n",
            " - 0s - loss: 0.3950 - categorical_accuracy: 0.7715 - val_loss: 0.9517 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01052: saving model to weights\n",
            "Epoch 1053/2000\n",
            " - 0s - loss: 0.3910 - categorical_accuracy: 0.7796 - val_loss: 0.9716 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01053: saving model to weights\n",
            "Epoch 1054/2000\n",
            " - 0s - loss: 0.3938 - categorical_accuracy: 0.7737 - val_loss: 1.0241 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01054: saving model to weights\n",
            "Epoch 1055/2000\n",
            " - 0s - loss: 0.4104 - categorical_accuracy: 0.7704 - val_loss: 1.0591 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01055: saving model to weights\n",
            "Epoch 1056/2000\n",
            " - 0s - loss: 0.4406 - categorical_accuracy: 0.7681 - val_loss: 1.2222 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01056: saving model to weights\n",
            "Epoch 1057/2000\n",
            " - 0s - loss: 0.5292 - categorical_accuracy: 0.7552 - val_loss: 1.1294 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01057: saving model to weights\n",
            "Epoch 1058/2000\n",
            " - 0s - loss: 0.6577 - categorical_accuracy: 0.7381 - val_loss: 1.9881 - val_categorical_accuracy: 0.6054\n",
            "\n",
            "Epoch 01058: saving model to weights\n",
            "Epoch 1059/2000\n",
            " - 0s - loss: 0.9105 - categorical_accuracy: 0.6267 - val_loss: 0.9503 - val_categorical_accuracy: 0.5585\n",
            "\n",
            "Epoch 01059: saving model to weights\n",
            "Epoch 1060/2000\n",
            " - 0s - loss: 0.7314 - categorical_accuracy: 0.5770 - val_loss: 0.8965 - val_categorical_accuracy: 0.5518\n",
            "\n",
            "Epoch 01060: saving model to weights\n",
            "Epoch 1061/2000\n",
            " - 0s - loss: 0.7035 - categorical_accuracy: 0.6204 - val_loss: 0.9395 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01061: saving model to weights\n",
            "Epoch 1062/2000\n",
            " - 0s - loss: 0.6222 - categorical_accuracy: 0.7089 - val_loss: 0.9156 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01062: saving model to weights\n",
            "Epoch 1063/2000\n",
            " - 0s - loss: 0.5605 - categorical_accuracy: 0.7167 - val_loss: 0.8637 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01063: saving model to weights\n",
            "Epoch 1064/2000\n",
            " - 0s - loss: 0.5358 - categorical_accuracy: 0.7233 - val_loss: 1.1020 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01064: saving model to weights\n",
            "Epoch 1065/2000\n",
            " - 0s - loss: 0.4802 - categorical_accuracy: 0.7426 - val_loss: 0.9083 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01065: saving model to weights\n",
            "Epoch 1066/2000\n",
            " - 0s - loss: 0.4700 - categorical_accuracy: 0.7419 - val_loss: 0.9615 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01066: saving model to weights\n",
            "Epoch 1067/2000\n",
            " - 0s - loss: 0.4487 - categorical_accuracy: 0.7500 - val_loss: 0.9523 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01067: saving model to weights\n",
            "Epoch 1068/2000\n",
            " - 0s - loss: 0.4494 - categorical_accuracy: 0.7522 - val_loss: 1.0208 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01068: saving model to weights\n",
            "Epoch 1069/2000\n",
            " - 0s - loss: 0.4411 - categorical_accuracy: 0.7526 - val_loss: 0.9328 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01069: saving model to weights\n",
            "Epoch 1070/2000\n",
            " - 0s - loss: 0.4207 - categorical_accuracy: 0.7648 - val_loss: 0.9238 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01070: saving model to weights\n",
            "Epoch 1071/2000\n",
            " - 0s - loss: 0.4359 - categorical_accuracy: 0.7556 - val_loss: 0.9516 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01071: saving model to weights\n",
            "Epoch 1072/2000\n",
            " - 0s - loss: 0.4634 - categorical_accuracy: 0.7526 - val_loss: 0.8716 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01072: saving model to weights\n",
            "Epoch 1073/2000\n",
            " - 0s - loss: 0.4389 - categorical_accuracy: 0.7537 - val_loss: 0.8568 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01073: saving model to weights\n",
            "Epoch 1074/2000\n",
            " - 0s - loss: 0.4144 - categorical_accuracy: 0.7663 - val_loss: 0.9994 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01074: saving model to weights\n",
            "Epoch 1075/2000\n",
            " - 0s - loss: 0.4147 - categorical_accuracy: 0.7681 - val_loss: 1.0437 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01075: saving model to weights\n",
            "Epoch 1076/2000\n",
            " - 0s - loss: 0.4718 - categorical_accuracy: 0.7552 - val_loss: 1.0827 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01076: saving model to weights\n",
            "Epoch 1077/2000\n",
            " - 0s - loss: 0.4575 - categorical_accuracy: 0.7567 - val_loss: 1.0617 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01077: saving model to weights\n",
            "Epoch 1078/2000\n",
            " - 0s - loss: 0.4376 - categorical_accuracy: 0.7593 - val_loss: 0.9678 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01078: saving model to weights\n",
            "Epoch 1079/2000\n",
            " - 0s - loss: 0.4274 - categorical_accuracy: 0.7656 - val_loss: 0.9902 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01079: saving model to weights\n",
            "Epoch 1080/2000\n",
            " - 0s - loss: 0.4131 - categorical_accuracy: 0.7693 - val_loss: 1.0409 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01080: saving model to weights\n",
            "Epoch 1081/2000\n",
            " - 0s - loss: 0.4015 - categorical_accuracy: 0.7685 - val_loss: 0.9919 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01081: saving model to weights\n",
            "Epoch 1082/2000\n",
            " - 0s - loss: 0.4918 - categorical_accuracy: 0.7567 - val_loss: 1.1306 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01082: saving model to weights\n",
            "Epoch 1083/2000\n",
            " - 0s - loss: 0.5262 - categorical_accuracy: 0.7341 - val_loss: 1.1201 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01083: saving model to weights\n",
            "Epoch 1084/2000\n",
            " - 0s - loss: 0.4856 - categorical_accuracy: 0.7437 - val_loss: 1.0506 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01084: saving model to weights\n",
            "Epoch 1085/2000\n",
            " - 0s - loss: 0.4629 - categorical_accuracy: 0.7489 - val_loss: 1.1365 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01085: saving model to weights\n",
            "Epoch 1086/2000\n",
            " - 0s - loss: 0.4422 - categorical_accuracy: 0.7652 - val_loss: 1.0749 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01086: saving model to weights\n",
            "Epoch 1087/2000\n",
            " - 0s - loss: 0.4309 - categorical_accuracy: 0.7641 - val_loss: 1.0348 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01087: saving model to weights\n",
            "Epoch 1088/2000\n",
            " - 0s - loss: 0.4338 - categorical_accuracy: 0.7656 - val_loss: 1.0109 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01088: saving model to weights\n",
            "Epoch 1089/2000\n",
            " - 0s - loss: 0.4290 - categorical_accuracy: 0.7641 - val_loss: 0.8931 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01089: saving model to weights\n",
            "Epoch 1090/2000\n",
            " - 0s - loss: 0.4188 - categorical_accuracy: 0.7644 - val_loss: 1.0711 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 01090: saving model to weights\n",
            "Epoch 1091/2000\n",
            " - 0s - loss: 0.4144 - categorical_accuracy: 0.7704 - val_loss: 1.0252 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01091: saving model to weights\n",
            "Epoch 1092/2000\n",
            " - 0s - loss: 0.4064 - categorical_accuracy: 0.7704 - val_loss: 0.9898 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01092: saving model to weights\n",
            "Epoch 1093/2000\n",
            " - 0s - loss: 0.4039 - categorical_accuracy: 0.7693 - val_loss: 1.0301 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01093: saving model to weights\n",
            "Epoch 1094/2000\n",
            " - 0s - loss: 0.4021 - categorical_accuracy: 0.7707 - val_loss: 1.0165 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01094: saving model to weights\n",
            "Epoch 1095/2000\n",
            " - 0s - loss: 0.4050 - categorical_accuracy: 0.7674 - val_loss: 1.0243 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01095: saving model to weights\n",
            "Epoch 1096/2000\n",
            " - 0s - loss: 0.4019 - categorical_accuracy: 0.7678 - val_loss: 1.0549 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01096: saving model to weights\n",
            "Epoch 1097/2000\n",
            " - 0s - loss: 0.3992 - categorical_accuracy: 0.7715 - val_loss: 1.0826 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01097: saving model to weights\n",
            "Epoch 1098/2000\n",
            " - 0s - loss: 0.3969 - categorical_accuracy: 0.7685 - val_loss: 1.0475 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01098: saving model to weights\n",
            "Epoch 1099/2000\n",
            " - 0s - loss: 0.4045 - categorical_accuracy: 0.7667 - val_loss: 1.0885 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01099: saving model to weights\n",
            "Epoch 1100/2000\n",
            " - 0s - loss: 0.4021 - categorical_accuracy: 0.7674 - val_loss: 1.1645 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01100: saving model to weights\n",
            "Epoch 1101/2000\n",
            " - 0s - loss: 0.4029 - categorical_accuracy: 0.7656 - val_loss: 1.0392 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01101: saving model to weights\n",
            "Epoch 1102/2000\n",
            " - 0s - loss: 0.4013 - categorical_accuracy: 0.7648 - val_loss: 1.0878 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01102: saving model to weights\n",
            "Epoch 1103/2000\n",
            " - 0s - loss: 0.3945 - categorical_accuracy: 0.7730 - val_loss: 1.0838 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01103: saving model to weights\n",
            "Epoch 1104/2000\n",
            " - 0s - loss: 0.3963 - categorical_accuracy: 0.7667 - val_loss: 1.0608 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01104: saving model to weights\n",
            "Epoch 1105/2000\n",
            " - 0s - loss: 0.3975 - categorical_accuracy: 0.7670 - val_loss: 1.0607 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 01105: saving model to weights\n",
            "Epoch 1106/2000\n",
            " - 0s - loss: 0.3946 - categorical_accuracy: 0.7689 - val_loss: 0.9921 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01106: saving model to weights\n",
            "Epoch 1107/2000\n",
            " - 0s - loss: 0.3943 - categorical_accuracy: 0.7730 - val_loss: 1.0389 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01107: saving model to weights\n",
            "Epoch 1108/2000\n",
            " - 0s - loss: 0.3934 - categorical_accuracy: 0.7674 - val_loss: 1.0796 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 01108: saving model to weights\n",
            "Epoch 1109/2000\n",
            " - 0s - loss: 0.3931 - categorical_accuracy: 0.7719 - val_loss: 1.0266 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 01109: saving model to weights\n",
            "Epoch 1110/2000\n",
            " - 0s - loss: 0.3909 - categorical_accuracy: 0.7704 - val_loss: 1.1450 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01110: saving model to weights\n",
            "Epoch 1111/2000\n",
            " - 0s - loss: 0.3889 - categorical_accuracy: 0.7667 - val_loss: 1.1660 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01111: saving model to weights\n",
            "Epoch 1112/2000\n",
            " - 0s - loss: 0.3884 - categorical_accuracy: 0.7789 - val_loss: 1.1359 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01112: saving model to weights\n",
            "Epoch 1113/2000\n",
            " - 0s - loss: 0.3883 - categorical_accuracy: 0.7807 - val_loss: 1.0724 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01113: saving model to weights\n",
            "Epoch 1114/2000\n",
            " - 0s - loss: 0.3892 - categorical_accuracy: 0.7756 - val_loss: 1.1626 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01114: saving model to weights\n",
            "Epoch 1115/2000\n",
            " - 0s - loss: 0.3887 - categorical_accuracy: 0.7763 - val_loss: 1.0868 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01115: saving model to weights\n",
            "Epoch 1116/2000\n",
            " - 0s - loss: 0.3887 - categorical_accuracy: 0.7759 - val_loss: 1.1301 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01116: saving model to weights\n",
            "Epoch 1117/2000\n",
            " - 0s - loss: 0.3852 - categorical_accuracy: 0.7785 - val_loss: 1.1444 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01117: saving model to weights\n",
            "Epoch 1118/2000\n",
            " - 0s - loss: 0.3851 - categorical_accuracy: 0.7778 - val_loss: 1.1292 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01118: saving model to weights\n",
            "Epoch 1119/2000\n",
            " - 0s - loss: 0.3953 - categorical_accuracy: 0.7752 - val_loss: 1.0596 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01119: saving model to weights\n",
            "Epoch 1120/2000\n",
            " - 0s - loss: 0.3979 - categorical_accuracy: 0.7733 - val_loss: 1.0767 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01120: saving model to weights\n",
            "Epoch 1121/2000\n",
            " - 0s - loss: 0.4112 - categorical_accuracy: 0.7685 - val_loss: 1.0908 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01121: saving model to weights\n",
            "Epoch 1122/2000\n",
            " - 0s - loss: 0.3942 - categorical_accuracy: 0.7741 - val_loss: 1.0658 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01122: saving model to weights\n",
            "Epoch 1123/2000\n",
            " - 0s - loss: 0.3879 - categorical_accuracy: 0.7815 - val_loss: 1.1421 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01123: saving model to weights\n",
            "Epoch 1124/2000\n",
            " - 0s - loss: 0.3855 - categorical_accuracy: 0.7800 - val_loss: 1.0857 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01124: saving model to weights\n",
            "Epoch 1125/2000\n",
            " - 0s - loss: 0.3854 - categorical_accuracy: 0.7767 - val_loss: 1.1020 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01125: saving model to weights\n",
            "Epoch 1126/2000\n",
            " - 0s - loss: 0.3835 - categorical_accuracy: 0.7793 - val_loss: 1.1687 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01126: saving model to weights\n",
            "Epoch 1127/2000\n",
            " - 0s - loss: 0.3847 - categorical_accuracy: 0.7793 - val_loss: 1.1773 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01127: saving model to weights\n",
            "Epoch 1128/2000\n",
            " - 0s - loss: 0.3832 - categorical_accuracy: 0.7785 - val_loss: 1.1559 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01128: saving model to weights\n",
            "Epoch 1129/2000\n",
            " - 0s - loss: 0.3821 - categorical_accuracy: 0.7778 - val_loss: 1.1217 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01129: saving model to weights\n",
            "Epoch 1130/2000\n",
            " - 0s - loss: 0.3848 - categorical_accuracy: 0.7774 - val_loss: 1.1808 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01130: saving model to weights\n",
            "Epoch 1131/2000\n",
            " - 0s - loss: 0.3853 - categorical_accuracy: 0.7804 - val_loss: 1.2161 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01131: saving model to weights\n",
            "Epoch 1132/2000\n",
            " - 0s - loss: 0.3820 - categorical_accuracy: 0.7811 - val_loss: 1.1785 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01132: saving model to weights\n",
            "Epoch 1133/2000\n",
            " - 0s - loss: 0.3822 - categorical_accuracy: 0.7796 - val_loss: 1.2409 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01133: saving model to weights\n",
            "Epoch 1134/2000\n",
            " - 0s - loss: 0.3823 - categorical_accuracy: 0.7833 - val_loss: 1.2228 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01134: saving model to weights\n",
            "Epoch 1135/2000\n",
            " - 0s - loss: 0.3805 - categorical_accuracy: 0.7796 - val_loss: 1.1698 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01135: saving model to weights\n",
            "Epoch 1136/2000\n",
            " - 0s - loss: 0.3790 - categorical_accuracy: 0.7819 - val_loss: 1.1839 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01136: saving model to weights\n",
            "Epoch 1137/2000\n",
            " - 0s - loss: 0.3787 - categorical_accuracy: 0.7826 - val_loss: 1.1869 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01137: saving model to weights\n",
            "Epoch 1138/2000\n",
            " - 0s - loss: 0.3796 - categorical_accuracy: 0.7815 - val_loss: 1.1238 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01138: saving model to weights\n",
            "Epoch 1139/2000\n",
            " - 0s - loss: 0.3798 - categorical_accuracy: 0.7826 - val_loss: 1.1752 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01139: saving model to weights\n",
            "Epoch 1140/2000\n",
            " - 0s - loss: 0.3807 - categorical_accuracy: 0.7830 - val_loss: 1.2734 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01140: saving model to weights\n",
            "Epoch 1141/2000\n",
            " - 0s - loss: 0.3797 - categorical_accuracy: 0.7837 - val_loss: 1.2651 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01141: saving model to weights\n",
            "Epoch 1142/2000\n",
            " - 0s - loss: 0.3840 - categorical_accuracy: 0.7800 - val_loss: 1.2153 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01142: saving model to weights\n",
            "Epoch 1143/2000\n",
            " - 0s - loss: 0.3803 - categorical_accuracy: 0.7822 - val_loss: 1.2139 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01143: saving model to weights\n",
            "Epoch 1144/2000\n",
            " - 0s - loss: 0.3776 - categorical_accuracy: 0.7833 - val_loss: 1.2679 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01144: saving model to weights\n",
            "Epoch 1145/2000\n",
            " - 0s - loss: 0.3817 - categorical_accuracy: 0.7807 - val_loss: 1.0862 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01145: saving model to weights\n",
            "Epoch 1146/2000\n",
            " - 0s - loss: 0.3827 - categorical_accuracy: 0.7807 - val_loss: 1.0315 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01146: saving model to weights\n",
            "Epoch 1147/2000\n",
            " - 0s - loss: 0.3841 - categorical_accuracy: 0.7841 - val_loss: 1.1330 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01147: saving model to weights\n",
            "Epoch 1148/2000\n",
            " - 0s - loss: 0.3838 - categorical_accuracy: 0.7848 - val_loss: 1.1916 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01148: saving model to weights\n",
            "Epoch 1149/2000\n",
            " - 0s - loss: 0.3867 - categorical_accuracy: 0.7781 - val_loss: 1.1288 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01149: saving model to weights\n",
            "Epoch 1150/2000\n",
            " - 0s - loss: 0.3838 - categorical_accuracy: 0.7811 - val_loss: 1.0709 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01150: saving model to weights\n",
            "Epoch 1151/2000\n",
            " - 0s - loss: 0.3784 - categorical_accuracy: 0.7841 - val_loss: 1.1377 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01151: saving model to weights\n",
            "Epoch 1152/2000\n",
            " - 0s - loss: 0.3765 - categorical_accuracy: 0.7841 - val_loss: 1.1470 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01152: saving model to weights\n",
            "Epoch 1153/2000\n",
            " - 0s - loss: 0.3787 - categorical_accuracy: 0.7844 - val_loss: 1.1681 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01153: saving model to weights\n",
            "Epoch 1154/2000\n",
            " - 0s - loss: 0.3820 - categorical_accuracy: 0.7826 - val_loss: 1.2139 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01154: saving model to weights\n",
            "Epoch 1155/2000\n",
            " - 0s - loss: 0.3816 - categorical_accuracy: 0.7819 - val_loss: 1.1767 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01155: saving model to weights\n",
            "Epoch 1156/2000\n",
            " - 0s - loss: 0.3790 - categorical_accuracy: 0.7826 - val_loss: 1.1959 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01156: saving model to weights\n",
            "Epoch 1157/2000\n",
            " - 0s - loss: 0.3788 - categorical_accuracy: 0.7819 - val_loss: 1.1218 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01157: saving model to weights\n",
            "Epoch 1158/2000\n",
            " - 0s - loss: 0.3760 - categorical_accuracy: 0.7856 - val_loss: 1.1559 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01158: saving model to weights\n",
            "Epoch 1159/2000\n",
            " - 0s - loss: 0.3756 - categorical_accuracy: 0.7863 - val_loss: 1.1835 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01159: saving model to weights\n",
            "Epoch 1160/2000\n",
            " - 0s - loss: 0.3748 - categorical_accuracy: 0.7844 - val_loss: 1.2186 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01160: saving model to weights\n",
            "Epoch 1161/2000\n",
            " - 0s - loss: 0.3741 - categorical_accuracy: 0.7874 - val_loss: 1.2030 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01161: saving model to weights\n",
            "Epoch 1162/2000\n",
            " - 0s - loss: 0.3731 - categorical_accuracy: 0.7867 - val_loss: 1.2086 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01162: saving model to weights\n",
            "Epoch 1163/2000\n",
            " - 0s - loss: 0.3727 - categorical_accuracy: 0.7885 - val_loss: 1.1620 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01163: saving model to weights\n",
            "Epoch 1164/2000\n",
            " - 0s - loss: 0.3732 - categorical_accuracy: 0.7870 - val_loss: 1.1348 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01164: saving model to weights\n",
            "Epoch 1165/2000\n",
            " - 0s - loss: 0.3763 - categorical_accuracy: 0.7859 - val_loss: 1.1287 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01165: saving model to weights\n",
            "Epoch 1166/2000\n",
            " - 0s - loss: 0.3768 - categorical_accuracy: 0.7837 - val_loss: 1.1600 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01166: saving model to weights\n",
            "Epoch 1167/2000\n",
            " - 0s - loss: 0.3753 - categorical_accuracy: 0.7844 - val_loss: 1.2102 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01167: saving model to weights\n",
            "Epoch 1168/2000\n",
            " - 0s - loss: 0.3749 - categorical_accuracy: 0.7852 - val_loss: 1.1386 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01168: saving model to weights\n",
            "Epoch 1169/2000\n",
            " - 0s - loss: 0.3767 - categorical_accuracy: 0.7852 - val_loss: 1.1318 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01169: saving model to weights\n",
            "Epoch 1170/2000\n",
            " - 0s - loss: 0.3749 - categorical_accuracy: 0.7881 - val_loss: 1.1842 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01170: saving model to weights\n",
            "Epoch 1171/2000\n",
            " - 0s - loss: 0.3725 - categorical_accuracy: 0.7848 - val_loss: 1.2316 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01171: saving model to weights\n",
            "Epoch 1172/2000\n",
            " - 0s - loss: 0.3716 - categorical_accuracy: 0.7889 - val_loss: 1.2328 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01172: saving model to weights\n",
            "Epoch 1173/2000\n",
            " - 0s - loss: 0.3723 - categorical_accuracy: 0.7856 - val_loss: 1.2252 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01173: saving model to weights\n",
            "Epoch 1174/2000\n",
            " - 0s - loss: 0.3712 - categorical_accuracy: 0.7878 - val_loss: 1.2288 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01174: saving model to weights\n",
            "Epoch 1175/2000\n",
            " - 0s - loss: 0.3706 - categorical_accuracy: 0.7911 - val_loss: 1.2081 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01175: saving model to weights\n",
            "Epoch 1176/2000\n",
            " - 0s - loss: 0.3710 - categorical_accuracy: 0.7904 - val_loss: 1.2101 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01176: saving model to weights\n",
            "Epoch 1177/2000\n",
            " - 0s - loss: 0.3708 - categorical_accuracy: 0.7878 - val_loss: 1.2150 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01177: saving model to weights\n",
            "Epoch 1178/2000\n",
            " - 0s - loss: 0.3697 - categorical_accuracy: 0.7893 - val_loss: 1.2476 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01178: saving model to weights\n",
            "Epoch 1179/2000\n",
            " - 0s - loss: 0.3703 - categorical_accuracy: 0.7885 - val_loss: 1.2157 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01179: saving model to weights\n",
            "Epoch 1180/2000\n",
            " - 0s - loss: 0.3700 - categorical_accuracy: 0.7878 - val_loss: 1.2367 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01180: saving model to weights\n",
            "Epoch 1181/2000\n",
            " - 0s - loss: 0.3707 - categorical_accuracy: 0.7896 - val_loss: 1.1970 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01181: saving model to weights\n",
            "Epoch 1182/2000\n",
            " - 0s - loss: 0.3707 - categorical_accuracy: 0.7867 - val_loss: 1.1958 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01182: saving model to weights\n",
            "Epoch 1183/2000\n",
            " - 0s - loss: 0.3704 - categorical_accuracy: 0.7885 - val_loss: 1.2652 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01183: saving model to weights\n",
            "Epoch 1184/2000\n",
            " - 0s - loss: 0.3714 - categorical_accuracy: 0.7874 - val_loss: 1.3042 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01184: saving model to weights\n",
            "Epoch 1185/2000\n",
            " - 0s - loss: 0.3693 - categorical_accuracy: 0.7893 - val_loss: 1.2801 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01185: saving model to weights\n",
            "Epoch 1186/2000\n",
            " - 0s - loss: 0.3692 - categorical_accuracy: 0.7893 - val_loss: 1.2666 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01186: saving model to weights\n",
            "Epoch 1187/2000\n",
            " - 0s - loss: 0.3680 - categorical_accuracy: 0.7900 - val_loss: 1.2894 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01187: saving model to weights\n",
            "Epoch 1188/2000\n",
            " - 0s - loss: 0.3693 - categorical_accuracy: 0.7881 - val_loss: 1.2921 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01188: saving model to weights\n",
            "Epoch 1189/2000\n",
            " - 0s - loss: 0.3722 - categorical_accuracy: 0.7874 - val_loss: 1.2776 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01189: saving model to weights\n",
            "Epoch 1190/2000\n",
            " - 0s - loss: 0.3748 - categorical_accuracy: 0.7852 - val_loss: 1.1735 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01190: saving model to weights\n",
            "Epoch 1191/2000\n",
            " - 0s - loss: 0.3719 - categorical_accuracy: 0.7881 - val_loss: 1.2113 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01191: saving model to weights\n",
            "Epoch 1192/2000\n",
            " - 0s - loss: 0.3726 - categorical_accuracy: 0.7863 - val_loss: 1.2939 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01192: saving model to weights\n",
            "Epoch 1193/2000\n",
            " - 0s - loss: 0.3742 - categorical_accuracy: 0.7844 - val_loss: 1.1853 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01193: saving model to weights\n",
            "Epoch 1194/2000\n",
            " - 0s - loss: 0.3775 - categorical_accuracy: 0.7830 - val_loss: 1.1745 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01194: saving model to weights\n",
            "Epoch 1195/2000\n",
            " - 0s - loss: 0.3792 - categorical_accuracy: 0.7837 - val_loss: 1.3077 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01195: saving model to weights\n",
            "Epoch 1196/2000\n",
            " - 0s - loss: 0.3812 - categorical_accuracy: 0.7852 - val_loss: 1.3042 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01196: saving model to weights\n",
            "Epoch 1197/2000\n",
            " - 0s - loss: 0.4470 - categorical_accuracy: 0.7726 - val_loss: 1.8260 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01197: saving model to weights\n",
            "Epoch 1198/2000\n",
            " - 0s - loss: 0.5315 - categorical_accuracy: 0.7511 - val_loss: 1.4666 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01198: saving model to weights\n",
            "Epoch 1199/2000\n",
            " - 0s - loss: 0.5038 - categorical_accuracy: 0.7474 - val_loss: 1.3003 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01199: saving model to weights\n",
            "Epoch 1200/2000\n",
            " - 0s - loss: 0.4770 - categorical_accuracy: 0.7441 - val_loss: 1.2556 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01200: saving model to weights\n",
            "Epoch 1201/2000\n",
            " - 0s - loss: 0.5808 - categorical_accuracy: 0.7448 - val_loss: 1.1755 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01201: saving model to weights\n",
            "Epoch 1202/2000\n",
            " - 0s - loss: 0.5751 - categorical_accuracy: 0.7104 - val_loss: 1.1857 - val_categorical_accuracy: 0.5953\n",
            "\n",
            "Epoch 01202: saving model to weights\n",
            "Epoch 1203/2000\n",
            " - 0s - loss: 0.6841 - categorical_accuracy: 0.6448 - val_loss: 1.0811 - val_categorical_accuracy: 0.5920\n",
            "\n",
            "Epoch 01203: saving model to weights\n",
            "Epoch 1204/2000\n",
            " - 0s - loss: 0.6540 - categorical_accuracy: 0.6585 - val_loss: 1.1651 - val_categorical_accuracy: 0.6120\n",
            "\n",
            "Epoch 01204: saving model to weights\n",
            "Epoch 1205/2000\n",
            " - 0s - loss: 0.5955 - categorical_accuracy: 0.6837 - val_loss: 1.1319 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01205: saving model to weights\n",
            "Epoch 1206/2000\n",
            " - 0s - loss: 0.5571 - categorical_accuracy: 0.7022 - val_loss: 0.9935 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01206: saving model to weights\n",
            "Epoch 1207/2000\n",
            " - 0s - loss: 0.5386 - categorical_accuracy: 0.7226 - val_loss: 1.2491 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01207: saving model to weights\n",
            "Epoch 1208/2000\n",
            " - 0s - loss: 0.4963 - categorical_accuracy: 0.7459 - val_loss: 1.1273 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01208: saving model to weights\n",
            "Epoch 1209/2000\n",
            " - 0s - loss: 0.4863 - categorical_accuracy: 0.7552 - val_loss: 1.0163 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01209: saving model to weights\n",
            "Epoch 1210/2000\n",
            " - 0s - loss: 0.4730 - categorical_accuracy: 0.7581 - val_loss: 0.9729 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01210: saving model to weights\n",
            "Epoch 1211/2000\n",
            " - 0s - loss: 0.4593 - categorical_accuracy: 0.7619 - val_loss: 1.1059 - val_categorical_accuracy: 0.6355\n",
            "\n",
            "Epoch 01211: saving model to weights\n",
            "Epoch 1212/2000\n",
            " - 0s - loss: 0.5259 - categorical_accuracy: 0.7278 - val_loss: 0.9944 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01212: saving model to weights\n",
            "Epoch 1213/2000\n",
            " - 0s - loss: 0.4592 - categorical_accuracy: 0.7667 - val_loss: 0.9507 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01213: saving model to weights\n",
            "Epoch 1214/2000\n",
            " - 0s - loss: 0.4257 - categorical_accuracy: 0.7696 - val_loss: 0.9685 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01214: saving model to weights\n",
            "Epoch 1215/2000\n",
            " - 0s - loss: 0.4052 - categorical_accuracy: 0.7793 - val_loss: 1.0598 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01215: saving model to weights\n",
            "Epoch 1216/2000\n",
            " - 0s - loss: 0.3990 - categorical_accuracy: 0.7744 - val_loss: 1.0860 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01216: saving model to weights\n",
            "Epoch 1217/2000\n",
            " - 0s - loss: 0.4009 - categorical_accuracy: 0.7800 - val_loss: 0.9694 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01217: saving model to weights\n",
            "Epoch 1218/2000\n",
            " - 0s - loss: 0.3933 - categorical_accuracy: 0.7807 - val_loss: 0.9622 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01218: saving model to weights\n",
            "Epoch 1219/2000\n",
            " - 0s - loss: 0.3972 - categorical_accuracy: 0.7793 - val_loss: 1.0457 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01219: saving model to weights\n",
            "Epoch 1220/2000\n",
            " - 0s - loss: 0.3902 - categorical_accuracy: 0.7841 - val_loss: 1.0418 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01220: saving model to weights\n",
            "Epoch 1221/2000\n",
            " - 0s - loss: 0.5235 - categorical_accuracy: 0.7511 - val_loss: 1.4567 - val_categorical_accuracy: 0.5819\n",
            "\n",
            "Epoch 01221: saving model to weights\n",
            "Epoch 1222/2000\n",
            " - 0s - loss: 0.7442 - categorical_accuracy: 0.6396 - val_loss: 0.9853 - val_categorical_accuracy: 0.6054\n",
            "\n",
            "Epoch 01222: saving model to weights\n",
            "Epoch 1223/2000\n",
            " - 0s - loss: 0.6451 - categorical_accuracy: 0.6656 - val_loss: 1.0175 - val_categorical_accuracy: 0.6087\n",
            "\n",
            "Epoch 01223: saving model to weights\n",
            "Epoch 1224/2000\n",
            " - 0s - loss: 0.6029 - categorical_accuracy: 0.6585 - val_loss: 1.1107 - val_categorical_accuracy: 0.5886\n",
            "\n",
            "Epoch 01224: saving model to weights\n",
            "Epoch 1225/2000\n",
            " - 0s - loss: 0.5759 - categorical_accuracy: 0.6833 - val_loss: 1.0600 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01225: saving model to weights\n",
            "Epoch 1226/2000\n",
            " - 0s - loss: 0.5488 - categorical_accuracy: 0.7007 - val_loss: 1.0836 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01226: saving model to weights\n",
            "Epoch 1227/2000\n",
            " - 0s - loss: 0.5297 - categorical_accuracy: 0.7111 - val_loss: 1.1980 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01227: saving model to weights\n",
            "Epoch 1228/2000\n",
            " - 0s - loss: 0.5224 - categorical_accuracy: 0.7237 - val_loss: 1.1099 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01228: saving model to weights\n",
            "Epoch 1229/2000\n",
            " - 0s - loss: 0.4975 - categorical_accuracy: 0.7407 - val_loss: 1.1999 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01229: saving model to weights\n",
            "Epoch 1230/2000\n",
            " - 0s - loss: 0.4976 - categorical_accuracy: 0.7407 - val_loss: 1.0624 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01230: saving model to weights\n",
            "Epoch 1231/2000\n",
            " - 0s - loss: 0.4722 - categorical_accuracy: 0.7496 - val_loss: 1.1446 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01231: saving model to weights\n",
            "Epoch 1232/2000\n",
            " - 0s - loss: 0.4439 - categorical_accuracy: 0.7670 - val_loss: 1.2152 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01232: saving model to weights\n",
            "Epoch 1233/2000\n",
            " - 0s - loss: 0.4294 - categorical_accuracy: 0.7704 - val_loss: 1.1561 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01233: saving model to weights\n",
            "Epoch 1234/2000\n",
            " - 0s - loss: 0.4230 - categorical_accuracy: 0.7733 - val_loss: 1.1335 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01234: saving model to weights\n",
            "Epoch 1235/2000\n",
            " - 0s - loss: 0.4223 - categorical_accuracy: 0.7763 - val_loss: 1.1589 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01235: saving model to weights\n",
            "Epoch 1236/2000\n",
            " - 0s - loss: 0.4176 - categorical_accuracy: 0.7741 - val_loss: 1.1653 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01236: saving model to weights\n",
            "Epoch 1237/2000\n",
            " - 0s - loss: 0.4327 - categorical_accuracy: 0.7689 - val_loss: 1.1940 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01237: saving model to weights\n",
            "Epoch 1238/2000\n",
            " - 0s - loss: 0.4149 - categorical_accuracy: 0.7737 - val_loss: 1.1564 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01238: saving model to weights\n",
            "Epoch 1239/2000\n",
            " - 0s - loss: 0.4081 - categorical_accuracy: 0.7741 - val_loss: 1.1710 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01239: saving model to weights\n",
            "Epoch 1240/2000\n",
            " - 0s - loss: 0.4014 - categorical_accuracy: 0.7800 - val_loss: 1.2068 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01240: saving model to weights\n",
            "Epoch 1241/2000\n",
            " - 0s - loss: 0.3890 - categorical_accuracy: 0.7819 - val_loss: 1.0980 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01241: saving model to weights\n",
            "Epoch 1242/2000\n",
            " - 0s - loss: 0.3826 - categorical_accuracy: 0.7833 - val_loss: 1.2183 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01242: saving model to weights\n",
            "Epoch 1243/2000\n",
            " - 0s - loss: 0.3836 - categorical_accuracy: 0.7819 - val_loss: 1.2058 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01243: saving model to weights\n",
            "Epoch 1244/2000\n",
            " - 0s - loss: 0.3909 - categorical_accuracy: 0.7826 - val_loss: 1.2019 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01244: saving model to weights\n",
            "Epoch 1245/2000\n",
            " - 0s - loss: 0.3929 - categorical_accuracy: 0.7793 - val_loss: 1.1193 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01245: saving model to weights\n",
            "Epoch 1246/2000\n",
            " - 0s - loss: 0.4109 - categorical_accuracy: 0.7785 - val_loss: 1.2902 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01246: saving model to weights\n",
            "Epoch 1247/2000\n",
            " - 0s - loss: 0.4538 - categorical_accuracy: 0.7622 - val_loss: 1.3351 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01247: saving model to weights\n",
            "Epoch 1248/2000\n",
            " - 0s - loss: 0.4346 - categorical_accuracy: 0.7648 - val_loss: 1.1579 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01248: saving model to weights\n",
            "Epoch 1249/2000\n",
            " - 0s - loss: 0.4074 - categorical_accuracy: 0.7741 - val_loss: 1.1209 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01249: saving model to weights\n",
            "Epoch 1250/2000\n",
            " - 0s - loss: 0.3943 - categorical_accuracy: 0.7781 - val_loss: 1.2080 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01250: saving model to weights\n",
            "Epoch 1251/2000\n",
            " - 0s - loss: 0.3837 - categorical_accuracy: 0.7826 - val_loss: 1.2356 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01251: saving model to weights\n",
            "Epoch 1252/2000\n",
            " - 0s - loss: 0.3787 - categorical_accuracy: 0.7859 - val_loss: 1.1039 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01252: saving model to weights\n",
            "Epoch 1253/2000\n",
            " - 0s - loss: 0.3758 - categorical_accuracy: 0.7893 - val_loss: 1.2345 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01253: saving model to weights\n",
            "Epoch 1254/2000\n",
            " - 0s - loss: 0.3725 - categorical_accuracy: 0.7885 - val_loss: 1.2394 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01254: saving model to weights\n",
            "Epoch 1255/2000\n",
            " - 0s - loss: 0.3748 - categorical_accuracy: 0.7848 - val_loss: 1.1360 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01255: saving model to weights\n",
            "Epoch 1256/2000\n",
            " - 0s - loss: 0.3750 - categorical_accuracy: 0.7848 - val_loss: 1.1186 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01256: saving model to weights\n",
            "Epoch 1257/2000\n",
            " - 0s - loss: 0.3771 - categorical_accuracy: 0.7848 - val_loss: 1.2381 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01257: saving model to weights\n",
            "Epoch 1258/2000\n",
            " - 0s - loss: 0.3700 - categorical_accuracy: 0.7859 - val_loss: 1.1838 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01258: saving model to weights\n",
            "Epoch 1259/2000\n",
            " - 0s - loss: 0.3702 - categorical_accuracy: 0.7867 - val_loss: 1.2214 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01259: saving model to weights\n",
            "Epoch 1260/2000\n",
            " - 0s - loss: 0.3715 - categorical_accuracy: 0.7874 - val_loss: 1.3237 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01260: saving model to weights\n",
            "Epoch 1261/2000\n",
            " - 0s - loss: 0.3685 - categorical_accuracy: 0.7893 - val_loss: 1.1766 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01261: saving model to weights\n",
            "Epoch 1262/2000\n",
            " - 0s - loss: 0.3683 - categorical_accuracy: 0.7900 - val_loss: 1.2521 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01262: saving model to weights\n",
            "Epoch 1263/2000\n",
            " - 0s - loss: 0.3677 - categorical_accuracy: 0.7870 - val_loss: 1.3032 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01263: saving model to weights\n",
            "Epoch 1264/2000\n",
            " - 0s - loss: 0.3658 - categorical_accuracy: 0.7881 - val_loss: 1.2706 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01264: saving model to weights\n",
            "Epoch 1265/2000\n",
            " - 0s - loss: 0.3650 - categorical_accuracy: 0.7870 - val_loss: 1.2172 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01265: saving model to weights\n",
            "Epoch 1266/2000\n",
            " - 0s - loss: 0.3665 - categorical_accuracy: 0.7896 - val_loss: 1.3162 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01266: saving model to weights\n",
            "Epoch 1267/2000\n",
            " - 0s - loss: 0.3653 - categorical_accuracy: 0.7904 - val_loss: 1.2327 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01267: saving model to weights\n",
            "Epoch 1268/2000\n",
            " - 0s - loss: 0.3645 - categorical_accuracy: 0.7889 - val_loss: 1.2704 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01268: saving model to weights\n",
            "Epoch 1269/2000\n",
            " - 0s - loss: 0.3661 - categorical_accuracy: 0.7900 - val_loss: 1.3339 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01269: saving model to weights\n",
            "Epoch 1270/2000\n",
            " - 0s - loss: 0.3675 - categorical_accuracy: 0.7919 - val_loss: 1.1919 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01270: saving model to weights\n",
            "Epoch 1271/2000\n",
            " - 0s - loss: 0.3650 - categorical_accuracy: 0.7878 - val_loss: 1.2068 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01271: saving model to weights\n",
            "Epoch 1272/2000\n",
            " - 0s - loss: 0.3672 - categorical_accuracy: 0.7907 - val_loss: 1.3869 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01272: saving model to weights\n",
            "Epoch 1273/2000\n",
            " - 0s - loss: 0.3642 - categorical_accuracy: 0.7952 - val_loss: 1.2681 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01273: saving model to weights\n",
            "Epoch 1274/2000\n",
            " - 0s - loss: 0.3641 - categorical_accuracy: 0.7885 - val_loss: 1.2540 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01274: saving model to weights\n",
            "Epoch 1275/2000\n",
            " - 0s - loss: 0.3635 - categorical_accuracy: 0.7889 - val_loss: 1.2412 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01275: saving model to weights\n",
            "Epoch 1276/2000\n",
            " - 0s - loss: 0.3608 - categorical_accuracy: 0.7907 - val_loss: 1.2797 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01276: saving model to weights\n",
            "Epoch 1277/2000\n",
            " - 0s - loss: 0.3610 - categorical_accuracy: 0.7930 - val_loss: 1.2522 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01277: saving model to weights\n",
            "Epoch 1278/2000\n",
            " - 0s - loss: 0.3623 - categorical_accuracy: 0.7919 - val_loss: 1.3196 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01278: saving model to weights\n",
            "Epoch 1279/2000\n",
            " - 0s - loss: 0.3630 - categorical_accuracy: 0.7900 - val_loss: 1.2677 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01279: saving model to weights\n",
            "Epoch 1280/2000\n",
            " - 0s - loss: 0.3735 - categorical_accuracy: 0.7885 - val_loss: 1.0572 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01280: saving model to weights\n",
            "Epoch 1281/2000\n",
            " - 0s - loss: 0.3824 - categorical_accuracy: 0.7841 - val_loss: 1.0693 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01281: saving model to weights\n",
            "Epoch 1282/2000\n",
            " - 0s - loss: 0.3727 - categorical_accuracy: 0.7878 - val_loss: 1.1919 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01282: saving model to weights\n",
            "Epoch 1283/2000\n",
            " - 0s - loss: 0.3736 - categorical_accuracy: 0.7922 - val_loss: 1.1618 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01283: saving model to weights\n",
            "Epoch 1284/2000\n",
            " - 0s - loss: 0.3641 - categorical_accuracy: 0.7919 - val_loss: 1.2187 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01284: saving model to weights\n",
            "Epoch 1285/2000\n",
            " - 0s - loss: 0.4546 - categorical_accuracy: 0.7737 - val_loss: 1.2966 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01285: saving model to weights\n",
            "Epoch 1286/2000\n",
            " - 0s - loss: 0.4407 - categorical_accuracy: 0.7633 - val_loss: 1.1361 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01286: saving model to weights\n",
            "Epoch 1287/2000\n",
            " - 0s - loss: 0.4304 - categorical_accuracy: 0.7733 - val_loss: 1.2029 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01287: saving model to weights\n",
            "Epoch 1288/2000\n",
            " - 0s - loss: 0.4112 - categorical_accuracy: 0.7756 - val_loss: 1.1729 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01288: saving model to weights\n",
            "Epoch 1289/2000\n",
            " - 0s - loss: 0.4034 - categorical_accuracy: 0.7778 - val_loss: 1.2513 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01289: saving model to weights\n",
            "Epoch 1290/2000\n",
            " - 0s - loss: 0.3871 - categorical_accuracy: 0.7819 - val_loss: 1.2271 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01290: saving model to weights\n",
            "Epoch 1291/2000\n",
            " - 0s - loss: 0.3826 - categorical_accuracy: 0.7859 - val_loss: 1.2698 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01291: saving model to weights\n",
            "Epoch 1292/2000\n",
            " - 0s - loss: 0.3798 - categorical_accuracy: 0.7900 - val_loss: 1.2349 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01292: saving model to weights\n",
            "Epoch 1293/2000\n",
            " - 0s - loss: 0.3774 - categorical_accuracy: 0.7874 - val_loss: 1.3124 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01293: saving model to weights\n",
            "Epoch 1294/2000\n",
            " - 0s - loss: 0.3807 - categorical_accuracy: 0.7878 - val_loss: 1.3047 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01294: saving model to weights\n",
            "Epoch 1295/2000\n",
            " - 0s - loss: 0.4515 - categorical_accuracy: 0.7722 - val_loss: 1.3179 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01295: saving model to weights\n",
            "Epoch 1296/2000\n",
            " - 0s - loss: 0.5848 - categorical_accuracy: 0.7011 - val_loss: 1.1129 - val_categorical_accuracy: 0.6154\n",
            "\n",
            "Epoch 01296: saving model to weights\n",
            "Epoch 1297/2000\n",
            " - 0s - loss: 0.5413 - categorical_accuracy: 0.7204 - val_loss: 1.2104 - val_categorical_accuracy: 0.6388\n",
            "\n",
            "Epoch 01297: saving model to weights\n",
            "Epoch 1298/2000\n",
            " - 0s - loss: 0.5026 - categorical_accuracy: 0.7189 - val_loss: 1.2127 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01298: saving model to weights\n",
            "Epoch 1299/2000\n",
            " - 0s - loss: 0.4857 - categorical_accuracy: 0.7448 - val_loss: 1.1989 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01299: saving model to weights\n",
            "Epoch 1300/2000\n",
            " - 0s - loss: 0.4465 - categorical_accuracy: 0.7630 - val_loss: 1.1651 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01300: saving model to weights\n",
            "Epoch 1301/2000\n",
            " - 0s - loss: 0.4288 - categorical_accuracy: 0.7685 - val_loss: 1.1722 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01301: saving model to weights\n",
            "Epoch 1302/2000\n",
            " - 0s - loss: 0.4023 - categorical_accuracy: 0.7733 - val_loss: 1.1737 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01302: saving model to weights\n",
            "Epoch 1303/2000\n",
            " - 0s - loss: 0.3904 - categorical_accuracy: 0.7826 - val_loss: 1.1751 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01303: saving model to weights\n",
            "Epoch 1304/2000\n",
            " - 0s - loss: 0.3910 - categorical_accuracy: 0.7822 - val_loss: 1.2700 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01304: saving model to weights\n",
            "Epoch 1305/2000\n",
            " - 0s - loss: 0.3816 - categorical_accuracy: 0.7881 - val_loss: 1.2306 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01305: saving model to weights\n",
            "Epoch 1306/2000\n",
            " - 0s - loss: 0.3775 - categorical_accuracy: 0.7885 - val_loss: 1.2221 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01306: saving model to weights\n",
            "Epoch 1307/2000\n",
            " - 0s - loss: 0.3739 - categorical_accuracy: 0.7885 - val_loss: 1.2247 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01307: saving model to weights\n",
            "Epoch 1308/2000\n",
            " - 0s - loss: 0.3737 - categorical_accuracy: 0.7911 - val_loss: 1.2004 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01308: saving model to weights\n",
            "Epoch 1309/2000\n",
            " - 0s - loss: 0.3751 - categorical_accuracy: 0.7904 - val_loss: 1.2319 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01309: saving model to weights\n",
            "Epoch 1310/2000\n",
            " - 0s - loss: 0.3701 - categorical_accuracy: 0.7926 - val_loss: 1.3626 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01310: saving model to weights\n",
            "Epoch 1311/2000\n",
            " - 0s - loss: 0.3945 - categorical_accuracy: 0.7859 - val_loss: 1.1297 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01311: saving model to weights\n",
            "Epoch 1312/2000\n",
            " - 0s - loss: 0.4117 - categorical_accuracy: 0.7759 - val_loss: 1.2514 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01312: saving model to weights\n",
            "Epoch 1313/2000\n",
            " - 0s - loss: 0.4066 - categorical_accuracy: 0.7815 - val_loss: 1.3083 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01313: saving model to weights\n",
            "Epoch 1314/2000\n",
            " - 0s - loss: 0.3975 - categorical_accuracy: 0.7826 - val_loss: 1.2429 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01314: saving model to weights\n",
            "Epoch 1315/2000\n",
            " - 0s - loss: 0.3893 - categorical_accuracy: 0.7859 - val_loss: 1.0864 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01315: saving model to weights\n",
            "Epoch 1316/2000\n",
            " - 0s - loss: 0.4188 - categorical_accuracy: 0.7793 - val_loss: 1.1905 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01316: saving model to weights\n",
            "Epoch 1317/2000\n",
            " - 0s - loss: 0.4098 - categorical_accuracy: 0.7719 - val_loss: 1.3272 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01317: saving model to weights\n",
            "Epoch 1318/2000\n",
            " - 0s - loss: 0.3883 - categorical_accuracy: 0.7848 - val_loss: 1.1617 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01318: saving model to weights\n",
            "Epoch 1319/2000\n",
            " - 0s - loss: 0.3802 - categorical_accuracy: 0.7856 - val_loss: 1.2113 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01319: saving model to weights\n",
            "Epoch 1320/2000\n",
            " - 0s - loss: 0.3838 - categorical_accuracy: 0.7841 - val_loss: 1.2153 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01320: saving model to weights\n",
            "Epoch 1321/2000\n",
            " - 0s - loss: 0.3764 - categorical_accuracy: 0.7893 - val_loss: 1.1795 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01321: saving model to weights\n",
            "Epoch 1322/2000\n",
            " - 0s - loss: 0.3691 - categorical_accuracy: 0.7915 - val_loss: 1.2788 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01322: saving model to weights\n",
            "Epoch 1323/2000\n",
            " - 0s - loss: 0.3628 - categorical_accuracy: 0.7919 - val_loss: 1.2401 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01323: saving model to weights\n",
            "Epoch 1324/2000\n",
            " - 0s - loss: 0.3639 - categorical_accuracy: 0.7926 - val_loss: 1.1471 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01324: saving model to weights\n",
            "Epoch 1325/2000\n",
            " - 0s - loss: 0.3613 - categorical_accuracy: 0.7930 - val_loss: 1.2112 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01325: saving model to weights\n",
            "Epoch 1326/2000\n",
            " - 0s - loss: 0.3589 - categorical_accuracy: 0.7948 - val_loss: 1.2594 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01326: saving model to weights\n",
            "Epoch 1327/2000\n",
            " - 0s - loss: 0.3734 - categorical_accuracy: 0.7937 - val_loss: 1.2879 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01327: saving model to weights\n",
            "Epoch 1328/2000\n",
            " - 0s - loss: 0.5221 - categorical_accuracy: 0.7230 - val_loss: 1.2970 - val_categorical_accuracy: 0.6221\n",
            "\n",
            "Epoch 01328: saving model to weights\n",
            "Epoch 1329/2000\n",
            " - 0s - loss: 0.5166 - categorical_accuracy: 0.7311 - val_loss: 1.2737 - val_categorical_accuracy: 0.6321\n",
            "\n",
            "Epoch 01329: saving model to weights\n",
            "Epoch 1330/2000\n",
            " - 0s - loss: 0.4797 - categorical_accuracy: 0.7378 - val_loss: 1.3050 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01330: saving model to weights\n",
            "Epoch 1331/2000\n",
            " - 0s - loss: 0.4985 - categorical_accuracy: 0.7385 - val_loss: 1.2432 - val_categorical_accuracy: 0.6087\n",
            "\n",
            "Epoch 01331: saving model to weights\n",
            "Epoch 1332/2000\n",
            " - 0s - loss: 0.6652 - categorical_accuracy: 0.6370 - val_loss: 0.8732 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01332: saving model to weights\n",
            "Epoch 1333/2000\n",
            " - 0s - loss: 0.6285 - categorical_accuracy: 0.6919 - val_loss: 0.9175 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01333: saving model to weights\n",
            "Epoch 1334/2000\n",
            " - 0s - loss: 0.5228 - categorical_accuracy: 0.7330 - val_loss: 0.9289 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01334: saving model to weights\n",
            "Epoch 1335/2000\n",
            " - 0s - loss: 0.4961 - categorical_accuracy: 0.7415 - val_loss: 0.9217 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01335: saving model to weights\n",
            "Epoch 1336/2000\n",
            " - 0s - loss: 0.4756 - categorical_accuracy: 0.7493 - val_loss: 0.9161 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01336: saving model to weights\n",
            "Epoch 1337/2000\n",
            " - 0s - loss: 0.4645 - categorical_accuracy: 0.7552 - val_loss: 0.8933 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01337: saving model to weights\n",
            "Epoch 1338/2000\n",
            " - 0s - loss: 0.4730 - categorical_accuracy: 0.7519 - val_loss: 0.9568 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01338: saving model to weights\n",
            "Epoch 1339/2000\n",
            " - 0s - loss: 0.4481 - categorical_accuracy: 0.7615 - val_loss: 1.0892 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01339: saving model to weights\n",
            "Epoch 1340/2000\n",
            " - 0s - loss: 0.4503 - categorical_accuracy: 0.7619 - val_loss: 0.9821 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01340: saving model to weights\n",
            "Epoch 1341/2000\n",
            " - 0s - loss: 0.5202 - categorical_accuracy: 0.7415 - val_loss: 1.1243 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01341: saving model to weights\n",
            "Epoch 1342/2000\n",
            " - 0s - loss: 0.4565 - categorical_accuracy: 0.7563 - val_loss: 1.1455 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01342: saving model to weights\n",
            "Epoch 1343/2000\n",
            " - 0s - loss: 0.4334 - categorical_accuracy: 0.7685 - val_loss: 1.0013 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01343: saving model to weights\n",
            "Epoch 1344/2000\n",
            " - 0s - loss: 0.4113 - categorical_accuracy: 0.7759 - val_loss: 1.0722 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01344: saving model to weights\n",
            "Epoch 1345/2000\n",
            " - 0s - loss: 0.4031 - categorical_accuracy: 0.7811 - val_loss: 1.1790 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01345: saving model to weights\n",
            "Epoch 1346/2000\n",
            " - 0s - loss: 0.3934 - categorical_accuracy: 0.7819 - val_loss: 1.1010 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01346: saving model to weights\n",
            "Epoch 1347/2000\n",
            " - 0s - loss: 0.3861 - categorical_accuracy: 0.7830 - val_loss: 1.1112 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01347: saving model to weights\n",
            "Epoch 1348/2000\n",
            " - 0s - loss: 0.4055 - categorical_accuracy: 0.7830 - val_loss: 1.1125 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01348: saving model to weights\n",
            "Epoch 1349/2000\n",
            " - 0s - loss: 0.4096 - categorical_accuracy: 0.7811 - val_loss: 1.1195 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01349: saving model to weights\n",
            "Epoch 1350/2000\n",
            " - 0s - loss: 0.3984 - categorical_accuracy: 0.7811 - val_loss: 1.1510 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01350: saving model to weights\n",
            "Epoch 1351/2000\n",
            " - 0s - loss: 0.3855 - categorical_accuracy: 0.7852 - val_loss: 1.1207 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 01351: saving model to weights\n",
            "Epoch 1352/2000\n",
            " - 0s - loss: 0.3903 - categorical_accuracy: 0.7841 - val_loss: 1.0080 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01352: saving model to weights\n",
            "Epoch 1353/2000\n",
            " - 0s - loss: 0.4473 - categorical_accuracy: 0.7663 - val_loss: 1.0293 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01353: saving model to weights\n",
            "Epoch 1354/2000\n",
            " - 0s - loss: 0.4191 - categorical_accuracy: 0.7767 - val_loss: 1.1558 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01354: saving model to weights\n",
            "Epoch 1355/2000\n",
            " - 0s - loss: 0.4094 - categorical_accuracy: 0.7774 - val_loss: 1.0770 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01355: saving model to weights\n",
            "Epoch 1356/2000\n",
            " - 0s - loss: 0.4307 - categorical_accuracy: 0.7793 - val_loss: 1.0689 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01356: saving model to weights\n",
            "Epoch 1357/2000\n",
            " - 0s - loss: 0.4050 - categorical_accuracy: 0.7819 - val_loss: 1.1318 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01357: saving model to weights\n",
            "Epoch 1358/2000\n",
            " - 0s - loss: 0.3808 - categorical_accuracy: 0.7885 - val_loss: 1.1270 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01358: saving model to weights\n",
            "Epoch 1359/2000\n",
            " - 0s - loss: 0.3868 - categorical_accuracy: 0.7848 - val_loss: 1.2162 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01359: saving model to weights\n",
            "Epoch 1360/2000\n",
            " - 0s - loss: 0.5529 - categorical_accuracy: 0.7304 - val_loss: 1.0446 - val_categorical_accuracy: 0.6321\n",
            "\n",
            "Epoch 01360: saving model to weights\n",
            "Epoch 1361/2000\n",
            " - 0s - loss: 0.5133 - categorical_accuracy: 0.7315 - val_loss: 1.0443 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01361: saving model to weights\n",
            "Epoch 1362/2000\n",
            " - 0s - loss: 0.4569 - categorical_accuracy: 0.7519 - val_loss: 1.0230 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01362: saving model to weights\n",
            "Epoch 1363/2000\n",
            " - 0s - loss: 0.4295 - categorical_accuracy: 0.7656 - val_loss: 1.0594 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01363: saving model to weights\n",
            "Epoch 1364/2000\n",
            " - 0s - loss: 0.4039 - categorical_accuracy: 0.7807 - val_loss: 1.0473 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01364: saving model to weights\n",
            "Epoch 1365/2000\n",
            " - 0s - loss: 0.3985 - categorical_accuracy: 0.7807 - val_loss: 1.0444 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01365: saving model to weights\n",
            "Epoch 1366/2000\n",
            " - 0s - loss: 0.3887 - categorical_accuracy: 0.7837 - val_loss: 1.1682 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01366: saving model to weights\n",
            "Epoch 1367/2000\n",
            " - 0s - loss: 0.3990 - categorical_accuracy: 0.7856 - val_loss: 1.0932 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01367: saving model to weights\n",
            "Epoch 1368/2000\n",
            " - 0s - loss: 0.4013 - categorical_accuracy: 0.7804 - val_loss: 1.0626 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01368: saving model to weights\n",
            "Epoch 1369/2000\n",
            " - 0s - loss: 0.3938 - categorical_accuracy: 0.7896 - val_loss: 1.0962 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01369: saving model to weights\n",
            "Epoch 1370/2000\n",
            " - 0s - loss: 0.3834 - categorical_accuracy: 0.7870 - val_loss: 1.1499 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01370: saving model to weights\n",
            "Epoch 1371/2000\n",
            " - 0s - loss: 0.3722 - categorical_accuracy: 0.7911 - val_loss: 1.2135 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01371: saving model to weights\n",
            "Epoch 1372/2000\n",
            " - 0s - loss: 0.3782 - categorical_accuracy: 0.7900 - val_loss: 1.3512 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01372: saving model to weights\n",
            "Epoch 1373/2000\n",
            " - 0s - loss: 0.4436 - categorical_accuracy: 0.7656 - val_loss: 1.2461 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01373: saving model to weights\n",
            "Epoch 1374/2000\n",
            " - 0s - loss: 0.4247 - categorical_accuracy: 0.7748 - val_loss: 1.0867 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01374: saving model to weights\n",
            "Epoch 1375/2000\n",
            " - 0s - loss: 0.3975 - categorical_accuracy: 0.7852 - val_loss: 1.0701 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01375: saving model to weights\n",
            "Epoch 1376/2000\n",
            " - 0s - loss: 0.3833 - categorical_accuracy: 0.7889 - val_loss: 1.2058 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01376: saving model to weights\n",
            "Epoch 1377/2000\n",
            " - 0s - loss: 0.3730 - categorical_accuracy: 0.7922 - val_loss: 1.1804 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01377: saving model to weights\n",
            "Epoch 1378/2000\n",
            " - 0s - loss: 0.3692 - categorical_accuracy: 0.7956 - val_loss: 1.2404 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01378: saving model to weights\n",
            "Epoch 1379/2000\n",
            " - 0s - loss: 0.3670 - categorical_accuracy: 0.7970 - val_loss: 1.1838 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01379: saving model to weights\n",
            "Epoch 1380/2000\n",
            " - 0s - loss: 0.3729 - categorical_accuracy: 0.7919 - val_loss: 1.0905 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01380: saving model to weights\n",
            "Epoch 1381/2000\n",
            " - 0s - loss: 0.3725 - categorical_accuracy: 0.7919 - val_loss: 1.1127 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01381: saving model to weights\n",
            "Epoch 1382/2000\n",
            " - 0s - loss: 0.3689 - categorical_accuracy: 0.7944 - val_loss: 1.2467 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01382: saving model to weights\n",
            "Epoch 1383/2000\n",
            " - 0s - loss: 0.3730 - categorical_accuracy: 0.7907 - val_loss: 1.2704 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01383: saving model to weights\n",
            "Epoch 1384/2000\n",
            " - 0s - loss: 0.3775 - categorical_accuracy: 0.7904 - val_loss: 1.1858 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01384: saving model to weights\n",
            "Epoch 1385/2000\n",
            " - 0s - loss: 0.3779 - categorical_accuracy: 0.7878 - val_loss: 1.3281 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01385: saving model to weights\n",
            "Epoch 1386/2000\n",
            " - 0s - loss: 0.3747 - categorical_accuracy: 0.7907 - val_loss: 1.1262 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01386: saving model to weights\n",
            "Epoch 1387/2000\n",
            " - 0s - loss: 0.3655 - categorical_accuracy: 0.7915 - val_loss: 1.2125 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01387: saving model to weights\n",
            "Epoch 1388/2000\n",
            " - 0s - loss: 0.3597 - categorical_accuracy: 0.7978 - val_loss: 1.3141 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01388: saving model to weights\n",
            "Epoch 1389/2000\n",
            " - 0s - loss: 0.3592 - categorical_accuracy: 0.7963 - val_loss: 1.1756 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01389: saving model to weights\n",
            "Epoch 1390/2000\n",
            " - 0s - loss: 0.3587 - categorical_accuracy: 0.7970 - val_loss: 1.1265 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01390: saving model to weights\n",
            "Epoch 1391/2000\n",
            " - 0s - loss: 0.3593 - categorical_accuracy: 0.7989 - val_loss: 1.1834 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01391: saving model to weights\n",
            "Epoch 1392/2000\n",
            " - 0s - loss: 0.3585 - categorical_accuracy: 0.7967 - val_loss: 1.2676 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01392: saving model to weights\n",
            "Epoch 1393/2000\n",
            " - 0s - loss: 0.3572 - categorical_accuracy: 0.7989 - val_loss: 1.3922 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01393: saving model to weights\n",
            "Epoch 1394/2000\n",
            " - 0s - loss: 0.3570 - categorical_accuracy: 0.7974 - val_loss: 1.3330 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01394: saving model to weights\n",
            "Epoch 1395/2000\n",
            " - 0s - loss: 0.3560 - categorical_accuracy: 0.7963 - val_loss: 1.2755 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01395: saving model to weights\n",
            "Epoch 1396/2000\n",
            " - 0s - loss: 0.3548 - categorical_accuracy: 0.7993 - val_loss: 1.3029 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01396: saving model to weights\n",
            "Epoch 1397/2000\n",
            " - 0s - loss: 0.3544 - categorical_accuracy: 0.7989 - val_loss: 1.3417 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01397: saving model to weights\n",
            "Epoch 1398/2000\n",
            " - 0s - loss: 0.3559 - categorical_accuracy: 0.7985 - val_loss: 1.3258 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01398: saving model to weights\n",
            "Epoch 1399/2000\n",
            " - 0s - loss: 0.3560 - categorical_accuracy: 0.7985 - val_loss: 1.3066 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01399: saving model to weights\n",
            "Epoch 1400/2000\n",
            " - 0s - loss: 0.3544 - categorical_accuracy: 0.8000 - val_loss: 1.2960 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01400: saving model to weights\n",
            "Epoch 1401/2000\n",
            " - 0s - loss: 0.3537 - categorical_accuracy: 0.7993 - val_loss: 1.3517 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01401: saving model to weights\n",
            "Epoch 1402/2000\n",
            " - 0s - loss: 0.3521 - categorical_accuracy: 0.8000 - val_loss: 1.3872 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01402: saving model to weights\n",
            "Epoch 1403/2000\n",
            " - 0s - loss: 0.3539 - categorical_accuracy: 0.7985 - val_loss: 1.3680 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01403: saving model to weights\n",
            "Epoch 1404/2000\n",
            " - 0s - loss: 0.3546 - categorical_accuracy: 0.7978 - val_loss: 1.3481 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01404: saving model to weights\n",
            "Epoch 1405/2000\n",
            " - 0s - loss: 0.3558 - categorical_accuracy: 0.7993 - val_loss: 1.3266 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01405: saving model to weights\n",
            "Epoch 1406/2000\n",
            " - 0s - loss: 0.3574 - categorical_accuracy: 0.7985 - val_loss: 1.3329 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01406: saving model to weights\n",
            "Epoch 1407/2000\n",
            " - 0s - loss: 0.3560 - categorical_accuracy: 0.7978 - val_loss: 1.3195 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01407: saving model to weights\n",
            "Epoch 1408/2000\n",
            " - 0s - loss: 0.3519 - categorical_accuracy: 0.8007 - val_loss: 1.3915 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01408: saving model to weights\n",
            "Epoch 1409/2000\n",
            " - 0s - loss: 0.3511 - categorical_accuracy: 0.8000 - val_loss: 1.3899 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01409: saving model to weights\n",
            "Epoch 1410/2000\n",
            " - 0s - loss: 0.3514 - categorical_accuracy: 0.8004 - val_loss: 1.2631 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01410: saving model to weights\n",
            "Epoch 1411/2000\n",
            " - 0s - loss: 0.3522 - categorical_accuracy: 0.7985 - val_loss: 1.3564 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01411: saving model to weights\n",
            "Epoch 1412/2000\n",
            " - 0s - loss: 0.3528 - categorical_accuracy: 0.8007 - val_loss: 1.3771 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01412: saving model to weights\n",
            "Epoch 1413/2000\n",
            " - 0s - loss: 0.3527 - categorical_accuracy: 0.7993 - val_loss: 1.3370 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01413: saving model to weights\n",
            "Epoch 1414/2000\n",
            " - 0s - loss: 0.3524 - categorical_accuracy: 0.7996 - val_loss: 1.3699 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01414: saving model to weights\n",
            "Epoch 1415/2000\n",
            " - 0s - loss: 0.3563 - categorical_accuracy: 0.7974 - val_loss: 1.2920 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01415: saving model to weights\n",
            "Epoch 1416/2000\n",
            " - 0s - loss: 0.3757 - categorical_accuracy: 0.7956 - val_loss: 1.2136 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01416: saving model to weights\n",
            "Epoch 1417/2000\n",
            " - 0s - loss: 0.3945 - categorical_accuracy: 0.7893 - val_loss: 1.2679 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01417: saving model to weights\n",
            "Epoch 1418/2000\n",
            " - 0s - loss: 0.3779 - categorical_accuracy: 0.7952 - val_loss: 1.3063 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01418: saving model to weights\n",
            "Epoch 1419/2000\n",
            " - 0s - loss: 0.4255 - categorical_accuracy: 0.7759 - val_loss: 1.2945 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01419: saving model to weights\n",
            "Epoch 1420/2000\n",
            " - 0s - loss: 0.4395 - categorical_accuracy: 0.7648 - val_loss: 1.3186 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01420: saving model to weights\n",
            "Epoch 1421/2000\n",
            " - 0s - loss: 0.3948 - categorical_accuracy: 0.7800 - val_loss: 1.2829 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01421: saving model to weights\n",
            "Epoch 1422/2000\n",
            " - 0s - loss: 0.3973 - categorical_accuracy: 0.7844 - val_loss: 1.4746 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01422: saving model to weights\n",
            "Epoch 1423/2000\n",
            " - 0s - loss: 0.4088 - categorical_accuracy: 0.7800 - val_loss: 1.1449 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01423: saving model to weights\n",
            "Epoch 1424/2000\n",
            " - 0s - loss: 0.3909 - categorical_accuracy: 0.7856 - val_loss: 1.0736 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01424: saving model to weights\n",
            "Epoch 1425/2000\n",
            " - 0s - loss: 0.3720 - categorical_accuracy: 0.7944 - val_loss: 1.1721 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01425: saving model to weights\n",
            "Epoch 1426/2000\n",
            " - 0s - loss: 0.3686 - categorical_accuracy: 0.7937 - val_loss: 1.2889 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01426: saving model to weights\n",
            "Epoch 1427/2000\n",
            " - 0s - loss: 0.3634 - categorical_accuracy: 0.7937 - val_loss: 1.2717 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01427: saving model to weights\n",
            "Epoch 1428/2000\n",
            " - 0s - loss: 0.4391 - categorical_accuracy: 0.7856 - val_loss: 1.2993 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01428: saving model to weights\n",
            "Epoch 1429/2000\n",
            " - 0s - loss: 0.4284 - categorical_accuracy: 0.7685 - val_loss: 1.4076 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01429: saving model to weights\n",
            "Epoch 1430/2000\n",
            " - 0s - loss: 0.4169 - categorical_accuracy: 0.7811 - val_loss: 1.2060 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01430: saving model to weights\n",
            "Epoch 1431/2000\n",
            " - 0s - loss: 0.4461 - categorical_accuracy: 0.7756 - val_loss: 0.9434 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01431: saving model to weights\n",
            "Epoch 1432/2000\n",
            " - 0s - loss: 0.6022 - categorical_accuracy: 0.6759 - val_loss: 0.8990 - val_categorical_accuracy: 0.6087\n",
            "\n",
            "Epoch 01432: saving model to weights\n",
            "Epoch 1433/2000\n",
            " - 0s - loss: 0.5566 - categorical_accuracy: 0.7056 - val_loss: 0.9808 - val_categorical_accuracy: 0.6355\n",
            "\n",
            "Epoch 01433: saving model to weights\n",
            "Epoch 1434/2000\n",
            " - 0s - loss: 0.5016 - categorical_accuracy: 0.7222 - val_loss: 0.9337 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01434: saving model to weights\n",
            "Epoch 1435/2000\n",
            " - 0s - loss: 0.4614 - categorical_accuracy: 0.7433 - val_loss: 0.9550 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01435: saving model to weights\n",
            "Epoch 1436/2000\n",
            " - 0s - loss: 0.4367 - categorical_accuracy: 0.7611 - val_loss: 0.9479 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01436: saving model to weights\n",
            "Epoch 1437/2000\n",
            " - 0s - loss: 0.4114 - categorical_accuracy: 0.7681 - val_loss: 1.0030 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01437: saving model to weights\n",
            "Epoch 1438/2000\n",
            " - 0s - loss: 0.4156 - categorical_accuracy: 0.7685 - val_loss: 0.9505 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01438: saving model to weights\n",
            "Epoch 1439/2000\n",
            " - 0s - loss: 0.4143 - categorical_accuracy: 0.7800 - val_loss: 0.9966 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01439: saving model to weights\n",
            "Epoch 1440/2000\n",
            " - 0s - loss: 0.3879 - categorical_accuracy: 0.7819 - val_loss: 0.9277 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01440: saving model to weights\n",
            "Epoch 1441/2000\n",
            " - 0s - loss: 0.4191 - categorical_accuracy: 0.7815 - val_loss: 0.9416 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01441: saving model to weights\n",
            "Epoch 1442/2000\n",
            " - 0s - loss: 0.4040 - categorical_accuracy: 0.7841 - val_loss: 0.9820 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01442: saving model to weights\n",
            "Epoch 1443/2000\n",
            " - 0s - loss: 0.3916 - categorical_accuracy: 0.7856 - val_loss: 1.0557 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01443: saving model to weights\n",
            "Epoch 1444/2000\n",
            " - 0s - loss: 0.4414 - categorical_accuracy: 0.7674 - val_loss: 1.0536 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01444: saving model to weights\n",
            "Epoch 1445/2000\n",
            " - 0s - loss: 0.4189 - categorical_accuracy: 0.7774 - val_loss: 1.0400 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01445: saving model to weights\n",
            "Epoch 1446/2000\n",
            " - 0s - loss: 0.4492 - categorical_accuracy: 0.7719 - val_loss: 0.9564 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01446: saving model to weights\n",
            "Epoch 1447/2000\n",
            " - 0s - loss: 0.4187 - categorical_accuracy: 0.7759 - val_loss: 0.9370 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01447: saving model to weights\n",
            "Epoch 1448/2000\n",
            " - 0s - loss: 0.3937 - categorical_accuracy: 0.7874 - val_loss: 1.0429 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01448: saving model to weights\n",
            "Epoch 1449/2000\n",
            " - 0s - loss: 0.3839 - categorical_accuracy: 0.7907 - val_loss: 1.0059 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01449: saving model to weights\n",
            "Epoch 1450/2000\n",
            " - 0s - loss: 0.3696 - categorical_accuracy: 0.7948 - val_loss: 1.0067 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01450: saving model to weights\n",
            "Epoch 1451/2000\n",
            " - 0s - loss: 0.3611 - categorical_accuracy: 0.7978 - val_loss: 1.0540 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01451: saving model to weights\n",
            "Epoch 1452/2000\n",
            " - 0s - loss: 0.3627 - categorical_accuracy: 0.7963 - val_loss: 1.0783 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01452: saving model to weights\n",
            "Epoch 1453/2000\n",
            " - 0s - loss: 0.3595 - categorical_accuracy: 0.7970 - val_loss: 1.0440 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01453: saving model to weights\n",
            "Epoch 1454/2000\n",
            " - 0s - loss: 0.3554 - categorical_accuracy: 0.8000 - val_loss: 1.0547 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01454: saving model to weights\n",
            "Epoch 1455/2000\n",
            " - 0s - loss: 0.3712 - categorical_accuracy: 0.7959 - val_loss: 1.0314 - val_categorical_accuracy: 0.7191\n",
            "\n",
            "Epoch 01455: saving model to weights\n",
            "Epoch 1456/2000\n",
            " - 0s - loss: 0.6217 - categorical_accuracy: 0.7019 - val_loss: 0.9905 - val_categorical_accuracy: 0.5351\n",
            "\n",
            "Epoch 01456: saving model to weights\n",
            "Epoch 1457/2000\n",
            " - 0s - loss: 0.6829 - categorical_accuracy: 0.6137 - val_loss: 0.9732 - val_categorical_accuracy: 0.5753\n",
            "\n",
            "Epoch 01457: saving model to weights\n",
            "Epoch 1458/2000\n",
            " - 0s - loss: 0.6466 - categorical_accuracy: 0.6548 - val_loss: 0.9806 - val_categorical_accuracy: 0.5819\n",
            "\n",
            "Epoch 01458: saving model to weights\n",
            "Epoch 1459/2000\n",
            " - 0s - loss: 0.6032 - categorical_accuracy: 0.6689 - val_loss: 0.9792 - val_categorical_accuracy: 0.6120\n",
            "\n",
            "Epoch 01459: saving model to weights\n",
            "Epoch 1460/2000\n",
            " - 0s - loss: 0.5573 - categorical_accuracy: 0.6893 - val_loss: 0.8733 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01460: saving model to weights\n",
            "Epoch 1461/2000\n",
            " - 0s - loss: 0.5234 - categorical_accuracy: 0.7130 - val_loss: 0.9454 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01461: saving model to weights\n",
            "Epoch 1462/2000\n",
            " - 0s - loss: 0.4954 - categorical_accuracy: 0.7289 - val_loss: 1.0230 - val_categorical_accuracy: 0.6388\n",
            "\n",
            "Epoch 01462: saving model to weights\n",
            "Epoch 1463/2000\n",
            " - 0s - loss: 0.4835 - categorical_accuracy: 0.7300 - val_loss: 1.0281 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01463: saving model to weights\n",
            "Epoch 1464/2000\n",
            " - 0s - loss: 0.4626 - categorical_accuracy: 0.7515 - val_loss: 0.9980 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01464: saving model to weights\n",
            "Epoch 1465/2000\n",
            " - 0s - loss: 0.4576 - categorical_accuracy: 0.7674 - val_loss: 0.9613 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01465: saving model to weights\n",
            "Epoch 1466/2000\n",
            " - 0s - loss: 0.4505 - categorical_accuracy: 0.7578 - val_loss: 0.9917 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01466: saving model to weights\n",
            "Epoch 1467/2000\n",
            " - 0s - loss: 0.4413 - categorical_accuracy: 0.7630 - val_loss: 1.2680 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01467: saving model to weights\n",
            "Epoch 1468/2000\n",
            " - 0s - loss: 0.4607 - categorical_accuracy: 0.7674 - val_loss: 1.0006 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01468: saving model to weights\n",
            "Epoch 1469/2000\n",
            " - 0s - loss: 0.4859 - categorical_accuracy: 0.7537 - val_loss: 1.0344 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01469: saving model to weights\n",
            "Epoch 1470/2000\n",
            " - 0s - loss: 0.4601 - categorical_accuracy: 0.7478 - val_loss: 0.9916 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01470: saving model to weights\n",
            "Epoch 1471/2000\n",
            " - 0s - loss: 0.4381 - categorical_accuracy: 0.7541 - val_loss: 1.0577 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01471: saving model to weights\n",
            "Epoch 1472/2000\n",
            " - 0s - loss: 0.4160 - categorical_accuracy: 0.7726 - val_loss: 1.0067 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01472: saving model to weights\n",
            "Epoch 1473/2000\n",
            " - 0s - loss: 0.4004 - categorical_accuracy: 0.7774 - val_loss: 1.0576 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01473: saving model to weights\n",
            "Epoch 1474/2000\n",
            " - 0s - loss: 0.3901 - categorical_accuracy: 0.7867 - val_loss: 1.0414 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01474: saving model to weights\n",
            "Epoch 1475/2000\n",
            " - 0s - loss: 0.3914 - categorical_accuracy: 0.7867 - val_loss: 0.9575 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01475: saving model to weights\n",
            "Epoch 1476/2000\n",
            " - 0s - loss: 0.3956 - categorical_accuracy: 0.7830 - val_loss: 0.9678 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01476: saving model to weights\n",
            "Epoch 1477/2000\n",
            " - 0s - loss: 0.4493 - categorical_accuracy: 0.7681 - val_loss: 1.0043 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01477: saving model to weights\n",
            "Epoch 1478/2000\n",
            " - 0s - loss: 0.4265 - categorical_accuracy: 0.7763 - val_loss: 0.9637 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01478: saving model to weights\n",
            "Epoch 1479/2000\n",
            " - 0s - loss: 0.4081 - categorical_accuracy: 0.7778 - val_loss: 0.9005 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01479: saving model to weights\n",
            "Epoch 1480/2000\n",
            " - 0s - loss: 0.4152 - categorical_accuracy: 0.7837 - val_loss: 1.0700 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01480: saving model to weights\n",
            "Epoch 1481/2000\n",
            " - 0s - loss: 0.3974 - categorical_accuracy: 0.7881 - val_loss: 1.0557 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01481: saving model to weights\n",
            "Epoch 1482/2000\n",
            " - 0s - loss: 0.4392 - categorical_accuracy: 0.7756 - val_loss: 1.0752 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01482: saving model to weights\n",
            "Epoch 1483/2000\n",
            " - 0s - loss: 0.4450 - categorical_accuracy: 0.7641 - val_loss: 0.9860 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01483: saving model to weights\n",
            "Epoch 1484/2000\n",
            " - 0s - loss: 0.4070 - categorical_accuracy: 0.7841 - val_loss: 1.0975 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01484: saving model to weights\n",
            "Epoch 1485/2000\n",
            " - 0s - loss: 0.3914 - categorical_accuracy: 0.7870 - val_loss: 0.9612 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01485: saving model to weights\n",
            "Epoch 1486/2000\n",
            " - 0s - loss: 0.3749 - categorical_accuracy: 0.7900 - val_loss: 1.2767 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01486: saving model to weights\n",
            "Epoch 1487/2000\n",
            " - 0s - loss: 0.3922 - categorical_accuracy: 0.7837 - val_loss: 1.3511 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01487: saving model to weights\n",
            "Epoch 1488/2000\n",
            " - 0s - loss: 0.3797 - categorical_accuracy: 0.7881 - val_loss: 1.0535 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01488: saving model to weights\n",
            "Epoch 1489/2000\n",
            " - 0s - loss: 0.3844 - categorical_accuracy: 0.7896 - val_loss: 1.0895 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01489: saving model to weights\n",
            "Epoch 1490/2000\n",
            " - 0s - loss: 0.3661 - categorical_accuracy: 0.7970 - val_loss: 1.1440 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01490: saving model to weights\n",
            "Epoch 1491/2000\n",
            " - 0s - loss: 0.3609 - categorical_accuracy: 0.7959 - val_loss: 1.1162 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01491: saving model to weights\n",
            "Epoch 1492/2000\n",
            " - 0s - loss: 0.3548 - categorical_accuracy: 0.8019 - val_loss: 1.1181 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01492: saving model to weights\n",
            "Epoch 1493/2000\n",
            " - 0s - loss: 0.3537 - categorical_accuracy: 0.7993 - val_loss: 1.1537 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01493: saving model to weights\n",
            "Epoch 1494/2000\n",
            " - 0s - loss: 0.3531 - categorical_accuracy: 0.8000 - val_loss: 1.1769 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01494: saving model to weights\n",
            "Epoch 1495/2000\n",
            " - 0s - loss: 0.3724 - categorical_accuracy: 0.7970 - val_loss: 1.1797 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01495: saving model to weights\n",
            "Epoch 1496/2000\n",
            " - 0s - loss: 0.3653 - categorical_accuracy: 0.7956 - val_loss: 1.1032 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01496: saving model to weights\n",
            "Epoch 1497/2000\n",
            " - 0s - loss: 0.3609 - categorical_accuracy: 0.7981 - val_loss: 1.1787 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01497: saving model to weights\n",
            "Epoch 1498/2000\n",
            " - 0s - loss: 0.3600 - categorical_accuracy: 0.7996 - val_loss: 1.1404 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01498: saving model to weights\n",
            "Epoch 1499/2000\n",
            " - 0s - loss: 0.3619 - categorical_accuracy: 0.7967 - val_loss: 1.0365 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01499: saving model to weights\n",
            "Epoch 1500/2000\n",
            " - 0s - loss: 0.3555 - categorical_accuracy: 0.8048 - val_loss: 1.0838 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01500: saving model to weights\n",
            "Epoch 1501/2000\n",
            " - 0s - loss: 0.3595 - categorical_accuracy: 0.7993 - val_loss: 1.1409 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01501: saving model to weights\n",
            "Epoch 1502/2000\n",
            " - 0s - loss: 0.3583 - categorical_accuracy: 0.7981 - val_loss: 1.1324 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01502: saving model to weights\n",
            "Epoch 1503/2000\n",
            " - 0s - loss: 0.3564 - categorical_accuracy: 0.7985 - val_loss: 1.1490 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01503: saving model to weights\n",
            "Epoch 1504/2000\n",
            " - 0s - loss: 0.3489 - categorical_accuracy: 0.8000 - val_loss: 1.0638 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01504: saving model to weights\n",
            "Epoch 1505/2000\n",
            " - 0s - loss: 0.3475 - categorical_accuracy: 0.8026 - val_loss: 1.0929 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01505: saving model to weights\n",
            "Epoch 1506/2000\n",
            " - 0s - loss: 0.3454 - categorical_accuracy: 0.8041 - val_loss: 1.1136 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01506: saving model to weights\n",
            "Epoch 1507/2000\n",
            " - 0s - loss: 0.3445 - categorical_accuracy: 0.8030 - val_loss: 1.0752 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01507: saving model to weights\n",
            "Epoch 1508/2000\n",
            " - 0s - loss: 0.3431 - categorical_accuracy: 0.8063 - val_loss: 1.1408 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01508: saving model to weights\n",
            "Epoch 1509/2000\n",
            " - 0s - loss: 0.3433 - categorical_accuracy: 0.8030 - val_loss: 1.1227 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01509: saving model to weights\n",
            "Epoch 1510/2000\n",
            " - 0s - loss: 0.3439 - categorical_accuracy: 0.8030 - val_loss: 1.1038 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01510: saving model to weights\n",
            "Epoch 1511/2000\n",
            " - 0s - loss: 0.3429 - categorical_accuracy: 0.8056 - val_loss: 1.1252 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01511: saving model to weights\n",
            "Epoch 1512/2000\n",
            " - 0s - loss: 0.3429 - categorical_accuracy: 0.8056 - val_loss: 1.1338 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01512: saving model to weights\n",
            "Epoch 1513/2000\n",
            " - 0s - loss: 0.3425 - categorical_accuracy: 0.8074 - val_loss: 1.1365 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01513: saving model to weights\n",
            "Epoch 1514/2000\n",
            " - 0s - loss: 0.3441 - categorical_accuracy: 0.8048 - val_loss: 1.0993 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01514: saving model to weights\n",
            "Epoch 1515/2000\n",
            " - 0s - loss: 0.3441 - categorical_accuracy: 0.8048 - val_loss: 1.1497 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01515: saving model to weights\n",
            "Epoch 1516/2000\n",
            " - 0s - loss: 0.3440 - categorical_accuracy: 0.8037 - val_loss: 1.0901 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01516: saving model to weights\n",
            "Epoch 1517/2000\n",
            " - 0s - loss: 0.3458 - categorical_accuracy: 0.8063 - val_loss: 1.1372 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01517: saving model to weights\n",
            "Epoch 1518/2000\n",
            " - 0s - loss: 0.3956 - categorical_accuracy: 0.7859 - val_loss: 1.2849 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01518: saving model to weights\n",
            "Epoch 1519/2000\n",
            " - 0s - loss: 0.4415 - categorical_accuracy: 0.7748 - val_loss: 1.3344 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01519: saving model to weights\n",
            "Epoch 1520/2000\n",
            " - 0s - loss: 0.4220 - categorical_accuracy: 0.7826 - val_loss: 1.1736 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01520: saving model to weights\n",
            "Epoch 1521/2000\n",
            " - 0s - loss: 0.3983 - categorical_accuracy: 0.7944 - val_loss: 1.2883 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01521: saving model to weights\n",
            "Epoch 1522/2000\n",
            " - 0s - loss: 0.3893 - categorical_accuracy: 0.7900 - val_loss: 1.1022 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01522: saving model to weights\n",
            "Epoch 1523/2000\n",
            " - 0s - loss: 0.3705 - categorical_accuracy: 0.7941 - val_loss: 1.0784 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01523: saving model to weights\n",
            "Epoch 1524/2000\n",
            " - 0s - loss: 0.3622 - categorical_accuracy: 0.8007 - val_loss: 1.2872 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01524: saving model to weights\n",
            "Epoch 1525/2000\n",
            " - 0s - loss: 0.3588 - categorical_accuracy: 0.7989 - val_loss: 1.1820 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01525: saving model to weights\n",
            "Epoch 1526/2000\n",
            " - 0s - loss: 0.3651 - categorical_accuracy: 0.7978 - val_loss: 1.2990 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01526: saving model to weights\n",
            "Epoch 1527/2000\n",
            " - 0s - loss: 0.3596 - categorical_accuracy: 0.7989 - val_loss: 1.0877 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01527: saving model to weights\n",
            "Epoch 1528/2000\n",
            " - 0s - loss: 0.3517 - categorical_accuracy: 0.7993 - val_loss: 1.1264 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01528: saving model to weights\n",
            "Epoch 1529/2000\n",
            " - 0s - loss: 0.3606 - categorical_accuracy: 0.7967 - val_loss: 1.1721 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01529: saving model to weights\n",
            "Epoch 1530/2000\n",
            " - 0s - loss: 0.4283 - categorical_accuracy: 0.7822 - val_loss: 1.4046 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01530: saving model to weights\n",
            "Epoch 1531/2000\n",
            " - 0s - loss: 0.5652 - categorical_accuracy: 0.7215 - val_loss: 1.2226 - val_categorical_accuracy: 0.6355\n",
            "\n",
            "Epoch 01531: saving model to weights\n",
            "Epoch 1532/2000\n",
            " - 0s - loss: 0.5049 - categorical_accuracy: 0.7448 - val_loss: 1.1580 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01532: saving model to weights\n",
            "Epoch 1533/2000\n",
            " - 0s - loss: 0.4753 - categorical_accuracy: 0.7530 - val_loss: 1.3839 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01533: saving model to weights\n",
            "Epoch 1534/2000\n",
            " - 0s - loss: 0.4315 - categorical_accuracy: 0.7707 - val_loss: 1.2141 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01534: saving model to weights\n",
            "Epoch 1535/2000\n",
            " - 0s - loss: 0.4003 - categorical_accuracy: 0.7870 - val_loss: 1.0796 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01535: saving model to weights\n",
            "Epoch 1536/2000\n",
            " - 0s - loss: 0.3798 - categorical_accuracy: 0.7904 - val_loss: 1.2241 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01536: saving model to weights\n",
            "Epoch 1537/2000\n",
            " - 0s - loss: 0.4087 - categorical_accuracy: 0.7700 - val_loss: 1.1143 - val_categorical_accuracy: 0.6321\n",
            "\n",
            "Epoch 01537: saving model to weights\n",
            "Epoch 1538/2000\n",
            " - 0s - loss: 0.4066 - categorical_accuracy: 0.7767 - val_loss: 1.2066 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01538: saving model to weights\n",
            "Epoch 1539/2000\n",
            " - 0s - loss: 0.3769 - categorical_accuracy: 0.7915 - val_loss: 1.1533 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01539: saving model to weights\n",
            "Epoch 1540/2000\n",
            " - 0s - loss: 0.3705 - categorical_accuracy: 0.7948 - val_loss: 1.1140 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01540: saving model to weights\n",
            "Epoch 1541/2000\n",
            " - 0s - loss: 0.3675 - categorical_accuracy: 0.7989 - val_loss: 1.1875 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01541: saving model to weights\n",
            "Epoch 1542/2000\n",
            " - 0s - loss: 0.3556 - categorical_accuracy: 0.8022 - val_loss: 1.1337 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01542: saving model to weights\n",
            "Epoch 1543/2000\n",
            " - 0s - loss: 0.3578 - categorical_accuracy: 0.8019 - val_loss: 1.1473 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01543: saving model to weights\n",
            "Epoch 1544/2000\n",
            " - 0s - loss: 0.3546 - categorical_accuracy: 0.8022 - val_loss: 1.1340 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01544: saving model to weights\n",
            "Epoch 1545/2000\n",
            " - 0s - loss: 0.3746 - categorical_accuracy: 0.8004 - val_loss: 1.2186 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01545: saving model to weights\n",
            "Epoch 1546/2000\n",
            " - 0s - loss: 0.3745 - categorical_accuracy: 0.7970 - val_loss: 1.0868 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01546: saving model to weights\n",
            "Epoch 1547/2000\n",
            " - 0s - loss: 0.3667 - categorical_accuracy: 0.8004 - val_loss: 1.1675 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01547: saving model to weights\n",
            "Epoch 1548/2000\n",
            " - 0s - loss: 0.3562 - categorical_accuracy: 0.8015 - val_loss: 1.1497 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01548: saving model to weights\n",
            "Epoch 1549/2000\n",
            " - 0s - loss: 0.3481 - categorical_accuracy: 0.8030 - val_loss: 1.1065 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01549: saving model to weights\n",
            "Epoch 1550/2000\n",
            " - 0s - loss: 0.3545 - categorical_accuracy: 0.8026 - val_loss: 1.1846 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01550: saving model to weights\n",
            "Epoch 1551/2000\n",
            " - 0s - loss: 0.3429 - categorical_accuracy: 0.8041 - val_loss: 1.1677 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01551: saving model to weights\n",
            "Epoch 1552/2000\n",
            " - 0s - loss: 0.3447 - categorical_accuracy: 0.8063 - val_loss: 1.1789 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01552: saving model to weights\n",
            "Epoch 1553/2000\n",
            " - 0s - loss: 0.3411 - categorical_accuracy: 0.8067 - val_loss: 1.1403 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01553: saving model to weights\n",
            "Epoch 1554/2000\n",
            " - 0s - loss: 0.3397 - categorical_accuracy: 0.8089 - val_loss: 1.2062 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01554: saving model to weights\n",
            "Epoch 1555/2000\n",
            " - 0s - loss: 0.3816 - categorical_accuracy: 0.7963 - val_loss: 1.2525 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01555: saving model to weights\n",
            "Epoch 1556/2000\n",
            " - 0s - loss: 0.3522 - categorical_accuracy: 0.8048 - val_loss: 1.1160 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01556: saving model to weights\n",
            "Epoch 1557/2000\n",
            " - 0s - loss: 0.3490 - categorical_accuracy: 0.8056 - val_loss: 1.0572 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01557: saving model to weights\n",
            "Epoch 1558/2000\n",
            " - 0s - loss: 0.3482 - categorical_accuracy: 0.8030 - val_loss: 1.1558 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01558: saving model to weights\n",
            "Epoch 1559/2000\n",
            " - 0s - loss: 0.3447 - categorical_accuracy: 0.8059 - val_loss: 1.1466 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01559: saving model to weights\n",
            "Epoch 1560/2000\n",
            " - 0s - loss: 0.3461 - categorical_accuracy: 0.8074 - val_loss: 1.1737 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01560: saving model to weights\n",
            "Epoch 1561/2000\n",
            " - 0s - loss: 0.3470 - categorical_accuracy: 0.8056 - val_loss: 1.1788 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01561: saving model to weights\n",
            "Epoch 1562/2000\n",
            " - 0s - loss: 0.3415 - categorical_accuracy: 0.8085 - val_loss: 1.1382 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01562: saving model to weights\n",
            "Epoch 1563/2000\n",
            " - 0s - loss: 0.3392 - categorical_accuracy: 0.8100 - val_loss: 1.1001 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01563: saving model to weights\n",
            "Epoch 1564/2000\n",
            " - 0s - loss: 0.3394 - categorical_accuracy: 0.8093 - val_loss: 1.1098 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01564: saving model to weights\n",
            "Epoch 1565/2000\n",
            " - 0s - loss: 0.3394 - categorical_accuracy: 0.8107 - val_loss: 1.1992 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01565: saving model to weights\n",
            "Epoch 1566/2000\n",
            " - 0s - loss: 0.3366 - categorical_accuracy: 0.8107 - val_loss: 1.1301 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01566: saving model to weights\n",
            "Epoch 1567/2000\n",
            " - 0s - loss: 0.3515 - categorical_accuracy: 0.8070 - val_loss: 1.1636 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01567: saving model to weights\n",
            "Epoch 1568/2000\n",
            " - 0s - loss: 0.4132 - categorical_accuracy: 0.7996 - val_loss: 1.2234 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01568: saving model to weights\n",
            "Epoch 1569/2000\n",
            " - 0s - loss: 0.3717 - categorical_accuracy: 0.7974 - val_loss: 1.2221 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01569: saving model to weights\n",
            "Epoch 1570/2000\n",
            " - 0s - loss: 0.3568 - categorical_accuracy: 0.8041 - val_loss: 1.2383 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01570: saving model to weights\n",
            "Epoch 1571/2000\n",
            " - 0s - loss: 0.3487 - categorical_accuracy: 0.8085 - val_loss: 1.1449 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01571: saving model to weights\n",
            "Epoch 1572/2000\n",
            " - 0s - loss: 0.3466 - categorical_accuracy: 0.8070 - val_loss: 1.0844 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01572: saving model to weights\n",
            "Epoch 1573/2000\n",
            " - 0s - loss: 0.3474 - categorical_accuracy: 0.8111 - val_loss: 1.2430 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01573: saving model to weights\n",
            "Epoch 1574/2000\n",
            " - 0s - loss: 0.3478 - categorical_accuracy: 0.8070 - val_loss: 1.0861 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01574: saving model to weights\n",
            "Epoch 1575/2000\n",
            " - 0s - loss: 0.3440 - categorical_accuracy: 0.8063 - val_loss: 1.1571 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01575: saving model to weights\n",
            "Epoch 1576/2000\n",
            " - 0s - loss: 0.3369 - categorical_accuracy: 0.8100 - val_loss: 1.2007 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01576: saving model to weights\n",
            "Epoch 1577/2000\n",
            " - 0s - loss: 0.3399 - categorical_accuracy: 0.8104 - val_loss: 1.3214 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01577: saving model to weights\n",
            "Epoch 1578/2000\n",
            " - 0s - loss: 0.4911 - categorical_accuracy: 0.7526 - val_loss: 1.1804 - val_categorical_accuracy: 0.6321\n",
            "\n",
            "Epoch 01578: saving model to weights\n",
            "Epoch 1579/2000\n",
            " - 0s - loss: 0.4413 - categorical_accuracy: 0.7533 - val_loss: 1.2920 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01579: saving model to weights\n",
            "Epoch 1580/2000\n",
            " - 0s - loss: 0.4083 - categorical_accuracy: 0.7800 - val_loss: 1.1845 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01580: saving model to weights\n",
            "Epoch 1581/2000\n",
            " - 0s - loss: 0.3923 - categorical_accuracy: 0.7848 - val_loss: 1.1900 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01581: saving model to weights\n",
            "Epoch 1582/2000\n",
            " - 0s - loss: 0.3789 - categorical_accuracy: 0.7948 - val_loss: 1.1699 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01582: saving model to weights\n",
            "Epoch 1583/2000\n",
            " - 0s - loss: 0.3636 - categorical_accuracy: 0.8015 - val_loss: 1.2137 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01583: saving model to weights\n",
            "Epoch 1584/2000\n",
            " - 0s - loss: 0.3604 - categorical_accuracy: 0.8044 - val_loss: 1.1652 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01584: saving model to weights\n",
            "Epoch 1585/2000\n",
            " - 0s - loss: 0.3526 - categorical_accuracy: 0.8048 - val_loss: 1.1263 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01585: saving model to weights\n",
            "Epoch 1586/2000\n",
            " - 0s - loss: 0.3504 - categorical_accuracy: 0.8067 - val_loss: 1.2314 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01586: saving model to weights\n",
            "Epoch 1587/2000\n",
            " - 0s - loss: 0.3383 - categorical_accuracy: 0.8115 - val_loss: 1.1652 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01587: saving model to weights\n",
            "Epoch 1588/2000\n",
            " - 0s - loss: 0.3400 - categorical_accuracy: 0.8126 - val_loss: 1.1885 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01588: saving model to weights\n",
            "Epoch 1589/2000\n",
            " - 0s - loss: 0.3590 - categorical_accuracy: 0.8081 - val_loss: 1.1933 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01589: saving model to weights\n",
            "Epoch 1590/2000\n",
            " - 0s - loss: 0.3457 - categorical_accuracy: 0.8104 - val_loss: 1.2592 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01590: saving model to weights\n",
            "Epoch 1591/2000\n",
            " - 0s - loss: 0.3395 - categorical_accuracy: 0.8111 - val_loss: 1.2334 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01591: saving model to weights\n",
            "Epoch 1592/2000\n",
            " - 0s - loss: 0.3347 - categorical_accuracy: 0.8152 - val_loss: 1.2339 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01592: saving model to weights\n",
            "Epoch 1593/2000\n",
            " - 0s - loss: 0.3569 - categorical_accuracy: 0.8085 - val_loss: 1.2321 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01593: saving model to weights\n",
            "Epoch 1594/2000\n",
            " - 0s - loss: 0.3517 - categorical_accuracy: 0.8041 - val_loss: 1.2266 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01594: saving model to weights\n",
            "Epoch 1595/2000\n",
            " - 0s - loss: 0.3451 - categorical_accuracy: 0.8048 - val_loss: 1.3988 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01595: saving model to weights\n",
            "Epoch 1596/2000\n",
            " - 0s - loss: 0.3834 - categorical_accuracy: 0.8052 - val_loss: 1.3644 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01596: saving model to weights\n",
            "Epoch 1597/2000\n",
            " - 0s - loss: 0.7591 - categorical_accuracy: 0.6656 - val_loss: 0.9515 - val_categorical_accuracy: 0.5251\n",
            "\n",
            "Epoch 01597: saving model to weights\n",
            "Epoch 1598/2000\n",
            " - 0s - loss: 0.8242 - categorical_accuracy: 0.5467 - val_loss: 1.0099 - val_categorical_accuracy: 0.5385\n",
            "\n",
            "Epoch 01598: saving model to weights\n",
            "Epoch 1599/2000\n",
            " - 0s - loss: 0.7960 - categorical_accuracy: 0.5544 - val_loss: 0.8617 - val_categorical_accuracy: 0.5452\n",
            "\n",
            "Epoch 01599: saving model to weights\n",
            "Epoch 1600/2000\n",
            " - 0s - loss: 0.7894 - categorical_accuracy: 0.5567 - val_loss: 0.8380 - val_categorical_accuracy: 0.5920\n",
            "\n",
            "Epoch 01600: saving model to weights\n",
            "Epoch 1601/2000\n",
            " - 0s - loss: 0.7070 - categorical_accuracy: 0.5904 - val_loss: 0.7903 - val_categorical_accuracy: 0.5920\n",
            "\n",
            "Epoch 01601: saving model to weights\n",
            "Epoch 1602/2000\n",
            " - 0s - loss: 0.6076 - categorical_accuracy: 0.6463 - val_loss: 0.7936 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01602: saving model to weights\n",
            "Epoch 1603/2000\n",
            " - 0s - loss: 0.9334 - categorical_accuracy: 0.6226 - val_loss: 0.9417 - val_categorical_accuracy: 0.4883\n",
            "\n",
            "Epoch 01603: saving model to weights\n",
            "Epoch 1604/2000\n",
            " - 0s - loss: 0.9014 - categorical_accuracy: 0.4900 - val_loss: 0.9415 - val_categorical_accuracy: 0.4749\n",
            "\n",
            "Epoch 01604: saving model to weights\n",
            "Epoch 1605/2000\n",
            " - 0s - loss: 0.9027 - categorical_accuracy: 0.4904 - val_loss: 0.9420 - val_categorical_accuracy: 0.4816\n",
            "\n",
            "Epoch 01605: saving model to weights\n",
            "Epoch 1606/2000\n",
            " - 0s - loss: 0.8951 - categorical_accuracy: 0.4933 - val_loss: 0.9432 - val_categorical_accuracy: 0.4883\n",
            "\n",
            "Epoch 01606: saving model to weights\n",
            "Epoch 1607/2000\n",
            " - 0s - loss: 0.8903 - categorical_accuracy: 0.4944 - val_loss: 0.9517 - val_categorical_accuracy: 0.4983\n",
            "\n",
            "Epoch 01607: saving model to weights\n",
            "Epoch 1608/2000\n",
            " - 0s - loss: 0.8846 - categorical_accuracy: 0.5156 - val_loss: 0.9500 - val_categorical_accuracy: 0.4983\n",
            "\n",
            "Epoch 01608: saving model to weights\n",
            "Epoch 1609/2000\n",
            " - 0s - loss: 0.8812 - categorical_accuracy: 0.5207 - val_loss: 0.9463 - val_categorical_accuracy: 0.5017\n",
            "\n",
            "Epoch 01609: saving model to weights\n",
            "Epoch 1610/2000\n",
            " - 0s - loss: 0.8766 - categorical_accuracy: 0.5244 - val_loss: 0.9511 - val_categorical_accuracy: 0.5017\n",
            "\n",
            "Epoch 01610: saving model to weights\n",
            "Epoch 1611/2000\n",
            " - 0s - loss: 0.8718 - categorical_accuracy: 0.5274 - val_loss: 0.9601 - val_categorical_accuracy: 0.5017\n",
            "\n",
            "Epoch 01611: saving model to weights\n",
            "Epoch 1612/2000\n",
            " - 0s - loss: 0.8686 - categorical_accuracy: 0.5311 - val_loss: 0.9523 - val_categorical_accuracy: 0.5084\n",
            "\n",
            "Epoch 01612: saving model to weights\n",
            "Epoch 1613/2000\n",
            " - 0s - loss: 0.8638 - categorical_accuracy: 0.5385 - val_loss: 0.9451 - val_categorical_accuracy: 0.5151\n",
            "\n",
            "Epoch 01613: saving model to weights\n",
            "Epoch 1614/2000\n",
            " - 0s - loss: 0.8577 - categorical_accuracy: 0.5404 - val_loss: 0.9460 - val_categorical_accuracy: 0.5217\n",
            "\n",
            "Epoch 01614: saving model to weights\n",
            "Epoch 1615/2000\n",
            " - 0s - loss: 0.8519 - categorical_accuracy: 0.5467 - val_loss: 0.9432 - val_categorical_accuracy: 0.5217\n",
            "\n",
            "Epoch 01615: saving model to weights\n",
            "Epoch 1616/2000\n",
            " - 0s - loss: 0.8448 - categorical_accuracy: 0.5533 - val_loss: 0.9138 - val_categorical_accuracy: 0.5385\n",
            "\n",
            "Epoch 01616: saving model to weights\n",
            "Epoch 1617/2000\n",
            " - 0s - loss: 0.8326 - categorical_accuracy: 0.5678 - val_loss: 0.9216 - val_categorical_accuracy: 0.5585\n",
            "\n",
            "Epoch 01617: saving model to weights\n",
            "Epoch 1618/2000\n",
            " - 0s - loss: 0.8068 - categorical_accuracy: 0.5952 - val_loss: 0.9098 - val_categorical_accuracy: 0.5953\n",
            "\n",
            "Epoch 01618: saving model to weights\n",
            "Epoch 1619/2000\n",
            " - 0s - loss: 0.7967 - categorical_accuracy: 0.6163 - val_loss: 0.8898 - val_categorical_accuracy: 0.5920\n",
            "\n",
            "Epoch 01619: saving model to weights\n",
            "Epoch 1620/2000\n",
            " - 0s - loss: 0.7868 - categorical_accuracy: 0.6096 - val_loss: 0.8716 - val_categorical_accuracy: 0.6054\n",
            "\n",
            "Epoch 01620: saving model to weights\n",
            "Epoch 1621/2000\n",
            " - 0s - loss: 0.7672 - categorical_accuracy: 0.6252 - val_loss: 0.8710 - val_categorical_accuracy: 0.6321\n",
            "\n",
            "Epoch 01621: saving model to weights\n",
            "Epoch 1622/2000\n",
            " - 0s - loss: 0.7325 - categorical_accuracy: 0.6578 - val_loss: 0.8347 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01622: saving model to weights\n",
            "Epoch 1623/2000\n",
            " - 0s - loss: 0.7106 - categorical_accuracy: 0.6730 - val_loss: 0.8247 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01623: saving model to weights\n",
            "Epoch 1624/2000\n",
            " - 0s - loss: 0.7160 - categorical_accuracy: 0.6659 - val_loss: 0.8176 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01624: saving model to weights\n",
            "Epoch 1625/2000\n",
            " - 0s - loss: 0.7367 - categorical_accuracy: 0.6596 - val_loss: 0.8430 - val_categorical_accuracy: 0.6154\n",
            "\n",
            "Epoch 01625: saving model to weights\n",
            "Epoch 1626/2000\n",
            " - 0s - loss: 0.7396 - categorical_accuracy: 0.6604 - val_loss: 0.9255 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01626: saving model to weights\n",
            "Epoch 1627/2000\n",
            " - 0s - loss: 0.6994 - categorical_accuracy: 0.6881 - val_loss: 0.8438 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01627: saving model to weights\n",
            "Epoch 1628/2000\n",
            " - 0s - loss: 0.6883 - categorical_accuracy: 0.6970 - val_loss: 0.7949 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01628: saving model to weights\n",
            "Epoch 1629/2000\n",
            " - 0s - loss: 0.6963 - categorical_accuracy: 0.6819 - val_loss: 0.7969 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01629: saving model to weights\n",
            "Epoch 1630/2000\n",
            " - 0s - loss: 0.6737 - categorical_accuracy: 0.7000 - val_loss: 0.7834 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01630: saving model to weights\n",
            "Epoch 1631/2000\n",
            " - 0s - loss: 0.6430 - categorical_accuracy: 0.7148 - val_loss: 0.7620 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01631: saving model to weights\n",
            "Epoch 1632/2000\n",
            " - 0s - loss: 0.6194 - categorical_accuracy: 0.7193 - val_loss: 0.7181 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01632: saving model to weights\n",
            "Epoch 1633/2000\n",
            " - 0s - loss: 0.6748 - categorical_accuracy: 0.6900 - val_loss: 0.8046 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01633: saving model to weights\n",
            "Epoch 1634/2000\n",
            " - 0s - loss: 0.7036 - categorical_accuracy: 0.6715 - val_loss: 0.8323 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01634: saving model to weights\n",
            "Epoch 1635/2000\n",
            " - 0s - loss: 0.6616 - categorical_accuracy: 0.7019 - val_loss: 0.8103 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01635: saving model to weights\n",
            "Epoch 1636/2000\n",
            " - 0s - loss: 0.5898 - categorical_accuracy: 0.7207 - val_loss: 0.7165 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01636: saving model to weights\n",
            "Epoch 1637/2000\n",
            " - 0s - loss: 0.5838 - categorical_accuracy: 0.7244 - val_loss: 0.7302 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01637: saving model to weights\n",
            "Epoch 1638/2000\n",
            " - 0s - loss: 0.6194 - categorical_accuracy: 0.7163 - val_loss: 0.7145 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01638: saving model to weights\n",
            "Epoch 1639/2000\n",
            " - 0s - loss: 0.5601 - categorical_accuracy: 0.7285 - val_loss: 0.7571 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01639: saving model to weights\n",
            "Epoch 1640/2000\n",
            " - 0s - loss: 0.8894 - categorical_accuracy: 0.7141 - val_loss: 1.6899 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01640: saving model to weights\n",
            "Epoch 1641/2000\n",
            " - 0s - loss: 0.8874 - categorical_accuracy: 0.7022 - val_loss: 0.8417 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01641: saving model to weights\n",
            "Epoch 1642/2000\n",
            " - 0s - loss: 0.6824 - categorical_accuracy: 0.7041 - val_loss: 0.7997 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01642: saving model to weights\n",
            "Epoch 1643/2000\n",
            " - 0s - loss: 0.6668 - categorical_accuracy: 0.7026 - val_loss: 0.7796 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01643: saving model to weights\n",
            "Epoch 1644/2000\n",
            " - 0s - loss: 0.6565 - categorical_accuracy: 0.7111 - val_loss: 0.7851 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01644: saving model to weights\n",
            "Epoch 1645/2000\n",
            " - 0s - loss: 0.6446 - categorical_accuracy: 0.7152 - val_loss: 0.7712 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01645: saving model to weights\n",
            "Epoch 1646/2000\n",
            " - 0s - loss: 0.6387 - categorical_accuracy: 0.7193 - val_loss: 0.7871 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01646: saving model to weights\n",
            "Epoch 1647/2000\n",
            " - 0s - loss: 0.6383 - categorical_accuracy: 0.7222 - val_loss: 0.7692 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01647: saving model to weights\n",
            "Epoch 1648/2000\n",
            " - 0s - loss: 0.6662 - categorical_accuracy: 0.6963 - val_loss: 0.8007 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01648: saving model to weights\n",
            "Epoch 1649/2000\n",
            " - 0s - loss: 0.6440 - categorical_accuracy: 0.7133 - val_loss: 0.7723 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01649: saving model to weights\n",
            "Epoch 1650/2000\n",
            " - 0s - loss: 0.6347 - categorical_accuracy: 0.7185 - val_loss: 0.8084 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01650: saving model to weights\n",
            "Epoch 1651/2000\n",
            " - 0s - loss: 0.6340 - categorical_accuracy: 0.7267 - val_loss: 0.7892 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01651: saving model to weights\n",
            "Epoch 1652/2000\n",
            " - 0s - loss: 0.6217 - categorical_accuracy: 0.7263 - val_loss: 0.7715 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01652: saving model to weights\n",
            "Epoch 1653/2000\n",
            " - 0s - loss: 0.6229 - categorical_accuracy: 0.7270 - val_loss: 0.7865 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01653: saving model to weights\n",
            "Epoch 1654/2000\n",
            " - 0s - loss: 0.6289 - categorical_accuracy: 0.7267 - val_loss: 0.7757 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01654: saving model to weights\n",
            "Epoch 1655/2000\n",
            " - 0s - loss: 0.6219 - categorical_accuracy: 0.7256 - val_loss: 0.7556 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01655: saving model to weights\n",
            "Epoch 1656/2000\n",
            " - 0s - loss: 0.6212 - categorical_accuracy: 0.7252 - val_loss: 0.7520 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01656: saving model to weights\n",
            "Epoch 1657/2000\n",
            " - 0s - loss: 0.6157 - categorical_accuracy: 0.7307 - val_loss: 0.7740 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01657: saving model to weights\n",
            "Epoch 1658/2000\n",
            " - 0s - loss: 0.6179 - categorical_accuracy: 0.7337 - val_loss: 0.7973 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01658: saving model to weights\n",
            "Epoch 1659/2000\n",
            " - 0s - loss: 0.6052 - categorical_accuracy: 0.7411 - val_loss: 0.7667 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01659: saving model to weights\n",
            "Epoch 1660/2000\n",
            " - 0s - loss: 0.6071 - categorical_accuracy: 0.7363 - val_loss: 0.7634 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01660: saving model to weights\n",
            "Epoch 1661/2000\n",
            " - 0s - loss: 0.6081 - categorical_accuracy: 0.7396 - val_loss: 0.7917 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01661: saving model to weights\n",
            "Epoch 1662/2000\n",
            " - 0s - loss: 0.6833 - categorical_accuracy: 0.6878 - val_loss: 0.8348 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01662: saving model to weights\n",
            "Epoch 1663/2000\n",
            " - 0s - loss: 0.6606 - categorical_accuracy: 0.7033 - val_loss: 0.8063 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01663: saving model to weights\n",
            "Epoch 1664/2000\n",
            " - 0s - loss: 0.6157 - categorical_accuracy: 0.7326 - val_loss: 0.7993 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01664: saving model to weights\n",
            "Epoch 1665/2000\n",
            " - 0s - loss: 0.6083 - categorical_accuracy: 0.7367 - val_loss: 0.7798 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01665: saving model to weights\n",
            "Epoch 1666/2000\n",
            " - 0s - loss: 0.6101 - categorical_accuracy: 0.7356 - val_loss: 0.8007 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01666: saving model to weights\n",
            "Epoch 1667/2000\n",
            " - 0s - loss: 0.6053 - categorical_accuracy: 0.7370 - val_loss: 0.7834 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01667: saving model to weights\n",
            "Epoch 1668/2000\n",
            " - 0s - loss: 0.6000 - categorical_accuracy: 0.7419 - val_loss: 0.7746 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01668: saving model to weights\n",
            "Epoch 1669/2000\n",
            " - 0s - loss: 0.5964 - categorical_accuracy: 0.7441 - val_loss: 0.7813 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01669: saving model to weights\n",
            "Epoch 1670/2000\n",
            " - 0s - loss: 0.5964 - categorical_accuracy: 0.7459 - val_loss: 0.7736 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01670: saving model to weights\n",
            "Epoch 1671/2000\n",
            " - 0s - loss: 0.5955 - categorical_accuracy: 0.7456 - val_loss: 0.7928 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01671: saving model to weights\n",
            "Epoch 1672/2000\n",
            " - 0s - loss: 0.5968 - categorical_accuracy: 0.7456 - val_loss: 0.7813 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01672: saving model to weights\n",
            "Epoch 1673/2000\n",
            " - 0s - loss: 0.5909 - categorical_accuracy: 0.7478 - val_loss: 0.7833 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01673: saving model to weights\n",
            "Epoch 1674/2000\n",
            " - 0s - loss: 0.5914 - categorical_accuracy: 0.7467 - val_loss: 0.7743 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01674: saving model to weights\n",
            "Epoch 1675/2000\n",
            " - 0s - loss: 0.5941 - categorical_accuracy: 0.7456 - val_loss: 0.7924 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01675: saving model to weights\n",
            "Epoch 1676/2000\n",
            " - 0s - loss: 0.5958 - categorical_accuracy: 0.7444 - val_loss: 0.7737 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01676: saving model to weights\n",
            "Epoch 1677/2000\n",
            " - 0s - loss: 0.5945 - categorical_accuracy: 0.7467 - val_loss: 0.7888 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01677: saving model to weights\n",
            "Epoch 1678/2000\n",
            " - 0s - loss: 0.5976 - categorical_accuracy: 0.7456 - val_loss: 0.7903 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01678: saving model to weights\n",
            "Epoch 1679/2000\n",
            " - 0s - loss: 0.6201 - categorical_accuracy: 0.7300 - val_loss: 0.8084 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01679: saving model to weights\n",
            "Epoch 1680/2000\n",
            " - 0s - loss: 0.6001 - categorical_accuracy: 0.7404 - val_loss: 0.8051 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01680: saving model to weights\n",
            "Epoch 1681/2000\n",
            " - 0s - loss: 0.6103 - categorical_accuracy: 0.7367 - val_loss: 0.7893 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01681: saving model to weights\n",
            "Epoch 1682/2000\n",
            " - 0s - loss: 0.6174 - categorical_accuracy: 0.7348 - val_loss: 0.8541 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01682: saving model to weights\n",
            "Epoch 1683/2000\n",
            " - 0s - loss: 0.6328 - categorical_accuracy: 0.7230 - val_loss: 0.8383 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01683: saving model to weights\n",
            "Epoch 1684/2000\n",
            " - 0s - loss: 0.6066 - categorical_accuracy: 0.7419 - val_loss: 0.7839 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01684: saving model to weights\n",
            "Epoch 1685/2000\n",
            " - 0s - loss: 0.6019 - categorical_accuracy: 0.7404 - val_loss: 0.7979 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01685: saving model to weights\n",
            "Epoch 1686/2000\n",
            " - 0s - loss: 0.6033 - categorical_accuracy: 0.7407 - val_loss: 0.7943 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01686: saving model to weights\n",
            "Epoch 1687/2000\n",
            " - 0s - loss: 0.5935 - categorical_accuracy: 0.7459 - val_loss: 0.7875 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01687: saving model to weights\n",
            "Epoch 1688/2000\n",
            " - 0s - loss: 0.5887 - categorical_accuracy: 0.7470 - val_loss: 0.7874 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01688: saving model to weights\n",
            "Epoch 1689/2000\n",
            " - 0s - loss: 0.5859 - categorical_accuracy: 0.7481 - val_loss: 0.7822 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01689: saving model to weights\n",
            "Epoch 1690/2000\n",
            " - 0s - loss: 0.5844 - categorical_accuracy: 0.7519 - val_loss: 0.7827 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01690: saving model to weights\n",
            "Epoch 1691/2000\n",
            " - 0s - loss: 0.5859 - categorical_accuracy: 0.7507 - val_loss: 0.7805 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01691: saving model to weights\n",
            "Epoch 1692/2000\n",
            " - 0s - loss: 0.5898 - categorical_accuracy: 0.7515 - val_loss: 0.8051 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01692: saving model to weights\n",
            "Epoch 1693/2000\n",
            " - 0s - loss: 0.5946 - categorical_accuracy: 0.7467 - val_loss: 0.8006 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01693: saving model to weights\n",
            "Epoch 1694/2000\n",
            " - 0s - loss: 0.5847 - categorical_accuracy: 0.7504 - val_loss: 0.7848 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01694: saving model to weights\n",
            "Epoch 1695/2000\n",
            " - 0s - loss: 0.5833 - categorical_accuracy: 0.7533 - val_loss: 0.7794 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01695: saving model to weights\n",
            "Epoch 1696/2000\n",
            " - 0s - loss: 0.5820 - categorical_accuracy: 0.7530 - val_loss: 0.7804 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01696: saving model to weights\n",
            "Epoch 1697/2000\n",
            " - 0s - loss: 0.5831 - categorical_accuracy: 0.7504 - val_loss: 0.7855 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01697: saving model to weights\n",
            "Epoch 1698/2000\n",
            " - 0s - loss: 0.5835 - categorical_accuracy: 0.7533 - val_loss: 0.7763 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01698: saving model to weights\n",
            "Epoch 1699/2000\n",
            " - 0s - loss: 0.5830 - categorical_accuracy: 0.7526 - val_loss: 0.7804 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01699: saving model to weights\n",
            "Epoch 1700/2000\n",
            " - 0s - loss: 0.5848 - categorical_accuracy: 0.7530 - val_loss: 0.7933 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01700: saving model to weights\n",
            "Epoch 1701/2000\n",
            " - 0s - loss: 0.5869 - categorical_accuracy: 0.7500 - val_loss: 0.7759 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01701: saving model to weights\n",
            "Epoch 1702/2000\n",
            " - 0s - loss: 0.5827 - categorical_accuracy: 0.7541 - val_loss: 0.8013 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01702: saving model to weights\n",
            "Epoch 1703/2000\n",
            " - 0s - loss: 0.5835 - categorical_accuracy: 0.7511 - val_loss: 0.7840 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01703: saving model to weights\n",
            "Epoch 1704/2000\n",
            " - 0s - loss: 0.5784 - categorical_accuracy: 0.7552 - val_loss: 0.7914 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01704: saving model to weights\n",
            "Epoch 1705/2000\n",
            " - 0s - loss: 0.5785 - categorical_accuracy: 0.7556 - val_loss: 0.7807 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01705: saving model to weights\n",
            "Epoch 1706/2000\n",
            " - 0s - loss: 0.5786 - categorical_accuracy: 0.7556 - val_loss: 0.7909 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01706: saving model to weights\n",
            "Epoch 1707/2000\n",
            " - 0s - loss: 0.5783 - categorical_accuracy: 0.7548 - val_loss: 0.7945 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01707: saving model to weights\n",
            "Epoch 1708/2000\n",
            " - 0s - loss: 0.5860 - categorical_accuracy: 0.7504 - val_loss: 0.7934 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01708: saving model to weights\n",
            "Epoch 1709/2000\n",
            " - 0s - loss: 0.5885 - categorical_accuracy: 0.7485 - val_loss: 0.8026 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01709: saving model to weights\n",
            "Epoch 1710/2000\n",
            " - 0s - loss: 0.5840 - categorical_accuracy: 0.7522 - val_loss: 0.7936 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01710: saving model to weights\n",
            "Epoch 1711/2000\n",
            " - 0s - loss: 0.5880 - categorical_accuracy: 0.7504 - val_loss: 0.8000 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01711: saving model to weights\n",
            "Epoch 1712/2000\n",
            " - 0s - loss: 0.5826 - categorical_accuracy: 0.7537 - val_loss: 0.7879 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01712: saving model to weights\n",
            "Epoch 1713/2000\n",
            " - 0s - loss: 0.5808 - categorical_accuracy: 0.7533 - val_loss: 0.7787 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01713: saving model to weights\n",
            "Epoch 1714/2000\n",
            " - 0s - loss: 0.5820 - categorical_accuracy: 0.7530 - val_loss: 0.8028 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01714: saving model to weights\n",
            "Epoch 1715/2000\n",
            " - 0s - loss: 0.6095 - categorical_accuracy: 0.7367 - val_loss: 0.8088 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01715: saving model to weights\n",
            "Epoch 1716/2000\n",
            " - 0s - loss: 0.5922 - categorical_accuracy: 0.7493 - val_loss: 0.8206 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01716: saving model to weights\n",
            "Epoch 1717/2000\n",
            " - 0s - loss: 0.6033 - categorical_accuracy: 0.7422 - val_loss: 0.8096 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01717: saving model to weights\n",
            "Epoch 1718/2000\n",
            " - 0s - loss: 0.5811 - categorical_accuracy: 0.7533 - val_loss: 0.7973 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 01718: saving model to weights\n",
            "Epoch 1719/2000\n",
            " - 0s - loss: 0.5849 - categorical_accuracy: 0.7500 - val_loss: 0.7846 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01719: saving model to weights\n",
            "Epoch 1720/2000\n",
            " - 0s - loss: 0.5780 - categorical_accuracy: 0.7541 - val_loss: 0.7968 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01720: saving model to weights\n",
            "Epoch 1721/2000\n",
            " - 0s - loss: 0.5821 - categorical_accuracy: 0.7533 - val_loss: 0.7926 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01721: saving model to weights\n",
            "Epoch 1722/2000\n",
            " - 0s - loss: 0.5813 - categorical_accuracy: 0.7533 - val_loss: 0.7858 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01722: saving model to weights\n",
            "Epoch 1723/2000\n",
            " - 0s - loss: 0.5747 - categorical_accuracy: 0.7578 - val_loss: 0.7926 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01723: saving model to weights\n",
            "Epoch 1724/2000\n",
            " - 0s - loss: 0.5728 - categorical_accuracy: 0.7589 - val_loss: 0.7886 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01724: saving model to weights\n",
            "Epoch 1725/2000\n",
            " - 0s - loss: 0.5785 - categorical_accuracy: 0.7570 - val_loss: 0.8001 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01725: saving model to weights\n",
            "Epoch 1726/2000\n",
            " - 0s - loss: 0.5733 - categorical_accuracy: 0.7570 - val_loss: 0.7885 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01726: saving model to weights\n",
            "Epoch 1727/2000\n",
            " - 0s - loss: 0.5726 - categorical_accuracy: 0.7585 - val_loss: 0.7924 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 01727: saving model to weights\n",
            "Epoch 1728/2000\n",
            " - 0s - loss: 0.5753 - categorical_accuracy: 0.7578 - val_loss: 0.7911 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01728: saving model to weights\n",
            "Epoch 1729/2000\n",
            " - 0s - loss: 0.5885 - categorical_accuracy: 0.7515 - val_loss: 0.8122 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01729: saving model to weights\n",
            "Epoch 1730/2000\n",
            " - 0s - loss: 0.5875 - categorical_accuracy: 0.7481 - val_loss: 0.8310 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01730: saving model to weights\n",
            "Epoch 1731/2000\n",
            " - 0s - loss: 0.8648 - categorical_accuracy: 0.7274 - val_loss: 0.8648 - val_categorical_accuracy: 0.6187\n",
            "\n",
            "Epoch 01731: saving model to weights\n",
            "Epoch 1732/2000\n",
            " - 0s - loss: 0.8371 - categorical_accuracy: 0.6015 - val_loss: 0.9192 - val_categorical_accuracy: 0.5652\n",
            "\n",
            "Epoch 01732: saving model to weights\n",
            "Epoch 1733/2000\n",
            " - 0s - loss: 0.8181 - categorical_accuracy: 0.6074 - val_loss: 0.8463 - val_categorical_accuracy: 0.6087\n",
            "\n",
            "Epoch 01733: saving model to weights\n",
            "Epoch 1734/2000\n",
            " - 0s - loss: 0.7674 - categorical_accuracy: 0.6356 - val_loss: 0.8166 - val_categorical_accuracy: 0.6221\n",
            "\n",
            "Epoch 01734: saving model to weights\n",
            "Epoch 1735/2000\n",
            " - 0s - loss: 0.7240 - categorical_accuracy: 0.6622 - val_loss: 0.8144 - val_categorical_accuracy: 0.6355\n",
            "\n",
            "Epoch 01735: saving model to weights\n",
            "Epoch 1736/2000\n",
            " - 0s - loss: 0.6991 - categorical_accuracy: 0.6741 - val_loss: 0.7980 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01736: saving model to weights\n",
            "Epoch 1737/2000\n",
            " - 0s - loss: 0.6755 - categorical_accuracy: 0.6893 - val_loss: 0.7949 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01737: saving model to weights\n",
            "Epoch 1738/2000\n",
            " - 0s - loss: 0.6675 - categorical_accuracy: 0.6933 - val_loss: 0.8081 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01738: saving model to weights\n",
            "Epoch 1739/2000\n",
            " - 0s - loss: 0.6595 - categorical_accuracy: 0.6944 - val_loss: 0.8203 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01739: saving model to weights\n",
            "Epoch 1740/2000\n",
            " - 0s - loss: 0.6717 - categorical_accuracy: 0.6885 - val_loss: 0.8562 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01740: saving model to weights\n",
            "Epoch 1741/2000\n",
            " - 0s - loss: 0.6578 - categorical_accuracy: 0.7000 - val_loss: 0.8560 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01741: saving model to weights\n",
            "Epoch 1742/2000\n",
            " - 0s - loss: 0.6786 - categorical_accuracy: 0.6870 - val_loss: 0.8530 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01742: saving model to weights\n",
            "Epoch 1743/2000\n",
            " - 0s - loss: 0.6525 - categorical_accuracy: 0.7022 - val_loss: 0.8905 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01743: saving model to weights\n",
            "Epoch 1744/2000\n",
            " - 0s - loss: 0.6663 - categorical_accuracy: 0.6911 - val_loss: 0.8487 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01744: saving model to weights\n",
            "Epoch 1745/2000\n",
            " - 0s - loss: 0.6461 - categorical_accuracy: 0.7048 - val_loss: 0.8513 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01745: saving model to weights\n",
            "Epoch 1746/2000\n",
            " - 0s - loss: 0.6567 - categorical_accuracy: 0.6974 - val_loss: 0.9139 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01746: saving model to weights\n",
            "Epoch 1747/2000\n",
            " - 0s - loss: 0.6423 - categorical_accuracy: 0.7085 - val_loss: 0.8279 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01747: saving model to weights\n",
            "Epoch 1748/2000\n",
            " - 0s - loss: 0.6395 - categorical_accuracy: 0.7096 - val_loss: 0.8080 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01748: saving model to weights\n",
            "Epoch 1749/2000\n",
            " - 0s - loss: 0.6326 - categorical_accuracy: 0.7141 - val_loss: 0.8201 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01749: saving model to weights\n",
            "Epoch 1750/2000\n",
            " - 0s - loss: 0.6358 - categorical_accuracy: 0.7126 - val_loss: 0.8236 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01750: saving model to weights\n",
            "Epoch 1751/2000\n",
            " - 0s - loss: 0.6317 - categorical_accuracy: 0.7130 - val_loss: 0.8364 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01751: saving model to weights\n",
            "Epoch 1752/2000\n",
            " - 0s - loss: 0.6311 - categorical_accuracy: 0.7152 - val_loss: 0.9058 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01752: saving model to weights\n",
            "Epoch 1753/2000\n",
            " - 0s - loss: 0.6421 - categorical_accuracy: 0.7100 - val_loss: 0.8541 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01753: saving model to weights\n",
            "Epoch 1754/2000\n",
            " - 0s - loss: 0.6333 - categorical_accuracy: 0.7133 - val_loss: 0.8529 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01754: saving model to weights\n",
            "Epoch 1755/2000\n",
            " - 0s - loss: 0.6505 - categorical_accuracy: 0.7015 - val_loss: 0.8575 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01755: saving model to weights\n",
            "Epoch 1756/2000\n",
            " - 0s - loss: 0.6695 - categorical_accuracy: 0.6881 - val_loss: 0.9119 - val_categorical_accuracy: 0.6254\n",
            "\n",
            "Epoch 01756: saving model to weights\n",
            "Epoch 1757/2000\n",
            " - 0s - loss: 0.7573 - categorical_accuracy: 0.6267 - val_loss: 1.0203 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01757: saving model to weights\n",
            "Epoch 1758/2000\n",
            " - 0s - loss: 0.6973 - categorical_accuracy: 0.6689 - val_loss: 1.0043 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01758: saving model to weights\n",
            "Epoch 1759/2000\n",
            " - 0s - loss: 0.6591 - categorical_accuracy: 0.7011 - val_loss: 0.9004 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01759: saving model to weights\n",
            "Epoch 1760/2000\n",
            " - 0s - loss: 0.7173 - categorical_accuracy: 0.6630 - val_loss: 0.8792 - val_categorical_accuracy: 0.6054\n",
            "\n",
            "Epoch 01760: saving model to weights\n",
            "Epoch 1761/2000\n",
            " - 0s - loss: 0.7584 - categorical_accuracy: 0.6285 - val_loss: 0.9078 - val_categorical_accuracy: 0.6221\n",
            "\n",
            "Epoch 01761: saving model to weights\n",
            "Epoch 1762/2000\n",
            " - 0s - loss: 0.6801 - categorical_accuracy: 0.6870 - val_loss: 0.9242 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01762: saving model to weights\n",
            "Epoch 1763/2000\n",
            " - 0s - loss: 0.6548 - categorical_accuracy: 0.7000 - val_loss: 0.8687 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01763: saving model to weights\n",
            "Epoch 1764/2000\n",
            " - 0s - loss: 0.6448 - categorical_accuracy: 0.7070 - val_loss: 0.8663 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01764: saving model to weights\n",
            "Epoch 1765/2000\n",
            " - 0s - loss: 0.6340 - categorical_accuracy: 0.7144 - val_loss: 0.9412 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01765: saving model to weights\n",
            "Epoch 1766/2000\n",
            " - 0s - loss: 0.6436 - categorical_accuracy: 0.7115 - val_loss: 0.9000 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01766: saving model to weights\n",
            "Epoch 1767/2000\n",
            " - 0s - loss: 0.6354 - categorical_accuracy: 0.7126 - val_loss: 0.9266 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01767: saving model to weights\n",
            "Epoch 1768/2000\n",
            " - 0s - loss: 0.6384 - categorical_accuracy: 0.7104 - val_loss: 0.9447 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01768: saving model to weights\n",
            "Epoch 1769/2000\n",
            " - 0s - loss: 0.6594 - categorical_accuracy: 0.6959 - val_loss: 0.9926 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01769: saving model to weights\n",
            "Epoch 1770/2000\n",
            " - 0s - loss: 0.6364 - categorical_accuracy: 0.7137 - val_loss: 0.8878 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01770: saving model to weights\n",
            "Epoch 1771/2000\n",
            " - 0s - loss: 0.6460 - categorical_accuracy: 0.7048 - val_loss: 0.8566 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01771: saving model to weights\n",
            "Epoch 1772/2000\n",
            " - 0s - loss: 0.6342 - categorical_accuracy: 0.7133 - val_loss: 0.8880 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01772: saving model to weights\n",
            "Epoch 1773/2000\n",
            " - 0s - loss: 0.6211 - categorical_accuracy: 0.7237 - val_loss: 0.8856 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01773: saving model to weights\n",
            "Epoch 1774/2000\n",
            " - 0s - loss: 0.6167 - categorical_accuracy: 0.7270 - val_loss: 0.8792 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01774: saving model to weights\n",
            "Epoch 1775/2000\n",
            " - 0s - loss: 0.6182 - categorical_accuracy: 0.7252 - val_loss: 0.8941 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01775: saving model to weights\n",
            "Epoch 1776/2000\n",
            " - 0s - loss: 0.6206 - categorical_accuracy: 0.7237 - val_loss: 0.9355 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01776: saving model to weights\n",
            "Epoch 1777/2000\n",
            " - 0s - loss: 0.6282 - categorical_accuracy: 0.7185 - val_loss: 0.9240 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01777: saving model to weights\n",
            "Epoch 1778/2000\n",
            " - 0s - loss: 0.6202 - categorical_accuracy: 0.7233 - val_loss: 0.9197 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01778: saving model to weights\n",
            "Epoch 1779/2000\n",
            " - 0s - loss: 0.6447 - categorical_accuracy: 0.7070 - val_loss: 0.9400 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01779: saving model to weights\n",
            "Epoch 1780/2000\n",
            " - 0s - loss: 0.6263 - categorical_accuracy: 0.7204 - val_loss: 0.9422 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01780: saving model to weights\n",
            "Epoch 1781/2000\n",
            " - 0s - loss: 0.6475 - categorical_accuracy: 0.7063 - val_loss: 0.9885 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01781: saving model to weights\n",
            "Epoch 1782/2000\n",
            " - 0s - loss: 0.6250 - categorical_accuracy: 0.7204 - val_loss: 0.9542 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01782: saving model to weights\n",
            "Epoch 1783/2000\n",
            " - 0s - loss: 0.6160 - categorical_accuracy: 0.7259 - val_loss: 0.9149 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01783: saving model to weights\n",
            "Epoch 1784/2000\n",
            " - 0s - loss: 0.6233 - categorical_accuracy: 0.7196 - val_loss: 0.9544 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01784: saving model to weights\n",
            "Epoch 1785/2000\n",
            " - 0s - loss: 0.6184 - categorical_accuracy: 0.7248 - val_loss: 0.9700 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01785: saving model to weights\n",
            "Epoch 1786/2000\n",
            " - 0s - loss: 0.6283 - categorical_accuracy: 0.7141 - val_loss: 0.9212 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01786: saving model to weights\n",
            "Epoch 1787/2000\n",
            " - 0s - loss: 0.6360 - categorical_accuracy: 0.7130 - val_loss: 0.9957 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01787: saving model to weights\n",
            "Epoch 1788/2000\n",
            " - 0s - loss: 0.6159 - categorical_accuracy: 0.7270 - val_loss: 0.8731 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01788: saving model to weights\n",
            "Epoch 1789/2000\n",
            " - 0s - loss: 0.6056 - categorical_accuracy: 0.7304 - val_loss: 0.9105 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01789: saving model to weights\n",
            "Epoch 1790/2000\n",
            " - 0s - loss: 0.5978 - categorical_accuracy: 0.7300 - val_loss: 0.9492 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01790: saving model to weights\n",
            "Epoch 1791/2000\n",
            " - 0s - loss: 0.5563 - categorical_accuracy: 0.7281 - val_loss: 0.8982 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01791: saving model to weights\n",
            "Epoch 1792/2000\n",
            " - 0s - loss: 0.5359 - categorical_accuracy: 0.7267 - val_loss: 0.9967 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01792: saving model to weights\n",
            "Epoch 1793/2000\n",
            " - 0s - loss: 0.5138 - categorical_accuracy: 0.7281 - val_loss: 0.9559 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01793: saving model to weights\n",
            "Epoch 1794/2000\n",
            " - 0s - loss: 0.5044 - categorical_accuracy: 0.7315 - val_loss: 0.9383 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01794: saving model to weights\n",
            "Epoch 1795/2000\n",
            " - 0s - loss: 0.5040 - categorical_accuracy: 0.7304 - val_loss: 0.9328 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01795: saving model to weights\n",
            "Epoch 1796/2000\n",
            " - 0s - loss: 0.5773 - categorical_accuracy: 0.7207 - val_loss: 1.1282 - val_categorical_accuracy: 0.6421\n",
            "\n",
            "Epoch 01796: saving model to weights\n",
            "Epoch 1797/2000\n",
            " - 0s - loss: 0.5477 - categorical_accuracy: 0.7244 - val_loss: 0.8939 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01797: saving model to weights\n",
            "Epoch 1798/2000\n",
            " - 0s - loss: 0.5681 - categorical_accuracy: 0.7104 - val_loss: 1.0616 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01798: saving model to weights\n",
            "Epoch 1799/2000\n",
            " - 0s - loss: 0.5562 - categorical_accuracy: 0.7170 - val_loss: 0.9120 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01799: saving model to weights\n",
            "Epoch 1800/2000\n",
            " - 0s - loss: 0.5450 - categorical_accuracy: 0.7170 - val_loss: 0.9144 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01800: saving model to weights\n",
            "Epoch 1801/2000\n",
            " - 0s - loss: 0.5688 - categorical_accuracy: 0.7089 - val_loss: 0.9276 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01801: saving model to weights\n",
            "Epoch 1802/2000\n",
            " - 0s - loss: 0.5410 - categorical_accuracy: 0.7204 - val_loss: 0.9824 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01802: saving model to weights\n",
            "Epoch 1803/2000\n",
            " - 0s - loss: 0.5524 - categorical_accuracy: 0.7152 - val_loss: 0.9543 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01803: saving model to weights\n",
            "Epoch 1804/2000\n",
            " - 0s - loss: 0.5700 - categorical_accuracy: 0.7015 - val_loss: 0.8359 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01804: saving model to weights\n",
            "Epoch 1805/2000\n",
            " - 0s - loss: 0.5464 - categorical_accuracy: 0.7133 - val_loss: 0.9391 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01805: saving model to weights\n",
            "Epoch 1806/2000\n",
            " - 0s - loss: 0.5414 - categorical_accuracy: 0.7159 - val_loss: 0.8947 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01806: saving model to weights\n",
            "Epoch 1807/2000\n",
            " - 0s - loss: 0.5349 - categorical_accuracy: 0.7178 - val_loss: 0.9596 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01807: saving model to weights\n",
            "Epoch 1808/2000\n",
            " - 0s - loss: 0.5342 - categorical_accuracy: 0.7196 - val_loss: 0.8703 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01808: saving model to weights\n",
            "Epoch 1809/2000\n",
            " - 0s - loss: 0.5112 - categorical_accuracy: 0.7285 - val_loss: 0.9067 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01809: saving model to weights\n",
            "Epoch 1810/2000\n",
            " - 0s - loss: 0.4992 - categorical_accuracy: 0.7304 - val_loss: 0.8419 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01810: saving model to weights\n",
            "Epoch 1811/2000\n",
            " - 0s - loss: 0.5002 - categorical_accuracy: 0.7326 - val_loss: 0.9404 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01811: saving model to weights\n",
            "Epoch 1812/2000\n",
            " - 0s - loss: 0.5342 - categorical_accuracy: 0.7233 - val_loss: 0.9669 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01812: saving model to weights\n",
            "Epoch 1813/2000\n",
            " - 0s - loss: 0.5102 - categorical_accuracy: 0.7300 - val_loss: 0.8931 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01813: saving model to weights\n",
            "Epoch 1814/2000\n",
            " - 0s - loss: 0.5157 - categorical_accuracy: 0.7263 - val_loss: 0.9424 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01814: saving model to weights\n",
            "Epoch 1815/2000\n",
            " - 0s - loss: 0.4990 - categorical_accuracy: 0.7281 - val_loss: 0.9453 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01815: saving model to weights\n",
            "Epoch 1816/2000\n",
            " - 0s - loss: 0.4872 - categorical_accuracy: 0.7326 - val_loss: 0.8503 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01816: saving model to weights\n",
            "Epoch 1817/2000\n",
            " - 0s - loss: 0.4840 - categorical_accuracy: 0.7341 - val_loss: 0.8885 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01817: saving model to weights\n",
            "Epoch 1818/2000\n",
            " - 0s - loss: 0.4828 - categorical_accuracy: 0.7344 - val_loss: 0.8717 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01818: saving model to weights\n",
            "Epoch 1819/2000\n",
            " - 0s - loss: 0.4786 - categorical_accuracy: 0.7363 - val_loss: 0.9063 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01819: saving model to weights\n",
            "Epoch 1820/2000\n",
            " - 0s - loss: 0.4764 - categorical_accuracy: 0.7367 - val_loss: 0.8869 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01820: saving model to weights\n",
            "Epoch 1821/2000\n",
            " - 0s - loss: 0.4817 - categorical_accuracy: 0.7348 - val_loss: 0.9327 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01821: saving model to weights\n",
            "Epoch 1822/2000\n",
            " - 0s - loss: 0.6556 - categorical_accuracy: 0.6867 - val_loss: 0.9699 - val_categorical_accuracy: 0.5853\n",
            "\n",
            "Epoch 01822: saving model to weights\n",
            "Epoch 1823/2000\n",
            " - 0s - loss: 0.7186 - categorical_accuracy: 0.6104 - val_loss: 0.9024 - val_categorical_accuracy: 0.5853\n",
            "\n",
            "Epoch 01823: saving model to weights\n",
            "Epoch 1824/2000\n",
            " - 0s - loss: 0.6732 - categorical_accuracy: 0.6333 - val_loss: 1.0394 - val_categorical_accuracy: 0.6355\n",
            "\n",
            "Epoch 01824: saving model to weights\n",
            "Epoch 1825/2000\n",
            " - 0s - loss: 0.6009 - categorical_accuracy: 0.6767 - val_loss: 0.9610 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01825: saving model to weights\n",
            "Epoch 1826/2000\n",
            " - 0s - loss: 0.5237 - categorical_accuracy: 0.7178 - val_loss: 0.8805 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01826: saving model to weights\n",
            "Epoch 1827/2000\n",
            " - 0s - loss: 0.5105 - categorical_accuracy: 0.7204 - val_loss: 0.8876 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01827: saving model to weights\n",
            "Epoch 1828/2000\n",
            " - 0s - loss: 0.5392 - categorical_accuracy: 0.7130 - val_loss: 0.9225 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01828: saving model to weights\n",
            "Epoch 1829/2000\n",
            " - 0s - loss: 0.5089 - categorical_accuracy: 0.7278 - val_loss: 0.9346 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01829: saving model to weights\n",
            "Epoch 1830/2000\n",
            " - 0s - loss: 0.4930 - categorical_accuracy: 0.7307 - val_loss: 0.8304 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01830: saving model to weights\n",
            "Epoch 1831/2000\n",
            " - 0s - loss: 0.4855 - categorical_accuracy: 0.7307 - val_loss: 0.8516 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01831: saving model to weights\n",
            "Epoch 1832/2000\n",
            " - 0s - loss: 0.4805 - categorical_accuracy: 0.7333 - val_loss: 0.8645 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01832: saving model to weights\n",
            "Epoch 1833/2000\n",
            " - 0s - loss: 0.4822 - categorical_accuracy: 0.7326 - val_loss: 0.8928 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01833: saving model to weights\n",
            "Epoch 1834/2000\n",
            " - 0s - loss: 0.4718 - categorical_accuracy: 0.7352 - val_loss: 0.8901 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01834: saving model to weights\n",
            "Epoch 1835/2000\n",
            " - 0s - loss: 0.4697 - categorical_accuracy: 0.7363 - val_loss: 0.8506 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01835: saving model to weights\n",
            "Epoch 1836/2000\n",
            " - 0s - loss: 0.4666 - categorical_accuracy: 0.7385 - val_loss: 0.8526 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01836: saving model to weights\n",
            "Epoch 1837/2000\n",
            " - 0s - loss: 0.4659 - categorical_accuracy: 0.7381 - val_loss: 0.8302 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01837: saving model to weights\n",
            "Epoch 1838/2000\n",
            " - 0s - loss: 0.4647 - categorical_accuracy: 0.7385 - val_loss: 0.8809 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01838: saving model to weights\n",
            "Epoch 1839/2000\n",
            " - 0s - loss: 0.4641 - categorical_accuracy: 0.7393 - val_loss: 0.8614 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01839: saving model to weights\n",
            "Epoch 1840/2000\n",
            " - 0s - loss: 0.4625 - categorical_accuracy: 0.7400 - val_loss: 0.8634 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01840: saving model to weights\n",
            "Epoch 1841/2000\n",
            " - 0s - loss: 0.4601 - categorical_accuracy: 0.7400 - val_loss: 0.8722 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01841: saving model to weights\n",
            "Epoch 1842/2000\n",
            " - 0s - loss: 0.4595 - categorical_accuracy: 0.7415 - val_loss: 0.8484 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01842: saving model to weights\n",
            "Epoch 1843/2000\n",
            " - 0s - loss: 0.4584 - categorical_accuracy: 0.7422 - val_loss: 0.8741 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01843: saving model to weights\n",
            "Epoch 1844/2000\n",
            " - 0s - loss: 0.4578 - categorical_accuracy: 0.7419 - val_loss: 0.8659 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01844: saving model to weights\n",
            "Epoch 1845/2000\n",
            " - 0s - loss: 0.4552 - categorical_accuracy: 0.7433 - val_loss: 0.8577 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01845: saving model to weights\n",
            "Epoch 1846/2000\n",
            " - 0s - loss: 0.4563 - categorical_accuracy: 0.7422 - val_loss: 0.8239 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01846: saving model to weights\n",
            "Epoch 1847/2000\n",
            " - 0s - loss: 0.4546 - categorical_accuracy: 0.7430 - val_loss: 0.8390 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01847: saving model to weights\n",
            "Epoch 1848/2000\n",
            " - 0s - loss: 0.4542 - categorical_accuracy: 0.7437 - val_loss: 0.8293 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01848: saving model to weights\n",
            "Epoch 1849/2000\n",
            " - 0s - loss: 0.4565 - categorical_accuracy: 0.7422 - val_loss: 0.8896 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01849: saving model to weights\n",
            "Epoch 1850/2000\n",
            " - 0s - loss: 0.4548 - categorical_accuracy: 0.7415 - val_loss: 0.8867 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01850: saving model to weights\n",
            "Epoch 1851/2000\n",
            " - 0s - loss: 0.4544 - categorical_accuracy: 0.7422 - val_loss: 0.8557 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01851: saving model to weights\n",
            "Epoch 1852/2000\n",
            " - 0s - loss: 0.4534 - categorical_accuracy: 0.7441 - val_loss: 0.8550 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01852: saving model to weights\n",
            "Epoch 1853/2000\n",
            " - 0s - loss: 0.4519 - categorical_accuracy: 0.7444 - val_loss: 0.9240 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01853: saving model to weights\n",
            "Epoch 1854/2000\n",
            " - 0s - loss: 0.4568 - categorical_accuracy: 0.7407 - val_loss: 0.8067 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01854: saving model to weights\n",
            "Epoch 1855/2000\n",
            " - 0s - loss: 0.4526 - categorical_accuracy: 0.7415 - val_loss: 0.9134 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01855: saving model to weights\n",
            "Epoch 1856/2000\n",
            " - 0s - loss: 0.4525 - categorical_accuracy: 0.7448 - val_loss: 0.8470 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01856: saving model to weights\n",
            "Epoch 1857/2000\n",
            " - 0s - loss: 0.4488 - categorical_accuracy: 0.7444 - val_loss: 0.9049 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01857: saving model to weights\n",
            "Epoch 1858/2000\n",
            " - 0s - loss: 0.4493 - categorical_accuracy: 0.7422 - val_loss: 0.8710 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01858: saving model to weights\n",
            "Epoch 1859/2000\n",
            " - 0s - loss: 0.4472 - categorical_accuracy: 0.7456 - val_loss: 0.9060 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01859: saving model to weights\n",
            "Epoch 1860/2000\n",
            " - 0s - loss: 0.4469 - categorical_accuracy: 0.7452 - val_loss: 0.8787 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01860: saving model to weights\n",
            "Epoch 1861/2000\n",
            " - 0s - loss: 0.4472 - categorical_accuracy: 0.7456 - val_loss: 0.9378 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01861: saving model to weights\n",
            "Epoch 1862/2000\n",
            " - 0s - loss: 0.4454 - categorical_accuracy: 0.7441 - val_loss: 0.8906 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01862: saving model to weights\n",
            "Epoch 1863/2000\n",
            " - 0s - loss: 0.4465 - categorical_accuracy: 0.7452 - val_loss: 0.9079 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01863: saving model to weights\n",
            "Epoch 1864/2000\n",
            " - 0s - loss: 0.4474 - categorical_accuracy: 0.7452 - val_loss: 0.9072 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01864: saving model to weights\n",
            "Epoch 1865/2000\n",
            " - 0s - loss: 0.4475 - categorical_accuracy: 0.7433 - val_loss: 0.9198 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01865: saving model to weights\n",
            "Epoch 1866/2000\n",
            " - 0s - loss: 0.4459 - categorical_accuracy: 0.7456 - val_loss: 0.8760 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01866: saving model to weights\n",
            "Epoch 1867/2000\n",
            " - 0s - loss: 0.4458 - categorical_accuracy: 0.7437 - val_loss: 0.9276 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01867: saving model to weights\n",
            "Epoch 1868/2000\n",
            " - 0s - loss: 0.4488 - categorical_accuracy: 0.7452 - val_loss: 0.9566 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01868: saving model to weights\n",
            "Epoch 1869/2000\n",
            " - 0s - loss: 0.4444 - categorical_accuracy: 0.7448 - val_loss: 0.9027 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01869: saving model to weights\n",
            "Epoch 1870/2000\n",
            " - 0s - loss: 0.4421 - categorical_accuracy: 0.7470 - val_loss: 0.8791 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01870: saving model to weights\n",
            "Epoch 1871/2000\n",
            " - 0s - loss: 0.4440 - categorical_accuracy: 0.7448 - val_loss: 0.9716 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01871: saving model to weights\n",
            "Epoch 1872/2000\n",
            " - 0s - loss: 0.4482 - categorical_accuracy: 0.7456 - val_loss: 1.0272 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01872: saving model to weights\n",
            "Epoch 1873/2000\n",
            " - 0s - loss: 0.4477 - categorical_accuracy: 0.7441 - val_loss: 0.8995 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01873: saving model to weights\n",
            "Epoch 1874/2000\n",
            " - 0s - loss: 0.7115 - categorical_accuracy: 0.6856 - val_loss: 0.9290 - val_categorical_accuracy: 0.6120\n",
            "\n",
            "Epoch 01874: saving model to weights\n",
            "Epoch 1875/2000\n",
            " - 0s - loss: 0.7157 - categorical_accuracy: 0.6541 - val_loss: 1.0086 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01875: saving model to weights\n",
            "Epoch 1876/2000\n",
            " - 0s - loss: 0.6831 - categorical_accuracy: 0.6922 - val_loss: 1.0318 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01876: saving model to weights\n",
            "Epoch 1877/2000\n",
            " - 0s - loss: 0.6295 - categorical_accuracy: 0.7230 - val_loss: 0.9928 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01877: saving model to weights\n",
            "Epoch 1878/2000\n",
            " - 0s - loss: 0.5682 - categorical_accuracy: 0.7256 - val_loss: 1.0274 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01878: saving model to weights\n",
            "Epoch 1879/2000\n",
            " - 0s - loss: 0.5163 - categorical_accuracy: 0.7289 - val_loss: 1.2897 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01879: saving model to weights\n",
            "Epoch 1880/2000\n",
            " - 0s - loss: 0.6234 - categorical_accuracy: 0.7104 - val_loss: 1.0700 - val_categorical_accuracy: 0.6722\n",
            "\n",
            "Epoch 01880: saving model to weights\n",
            "Epoch 1881/2000\n",
            " - 0s - loss: 0.5219 - categorical_accuracy: 0.7315 - val_loss: 0.9997 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01881: saving model to weights\n",
            "Epoch 1882/2000\n",
            " - 0s - loss: 0.4890 - categorical_accuracy: 0.7330 - val_loss: 0.8794 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01882: saving model to weights\n",
            "Epoch 1883/2000\n",
            " - 0s - loss: 0.4607 - categorical_accuracy: 0.7385 - val_loss: 1.0110 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01883: saving model to weights\n",
            "Epoch 1884/2000\n",
            " - 0s - loss: 0.4620 - categorical_accuracy: 0.7407 - val_loss: 0.8622 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01884: saving model to weights\n",
            "Epoch 1885/2000\n",
            " - 0s - loss: 0.4590 - categorical_accuracy: 0.7448 - val_loss: 0.8933 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01885: saving model to weights\n",
            "Epoch 1886/2000\n",
            " - 0s - loss: 0.4573 - categorical_accuracy: 0.7422 - val_loss: 0.8980 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01886: saving model to weights\n",
            "Epoch 1887/2000\n",
            " - 0s - loss: 0.4528 - categorical_accuracy: 0.7444 - val_loss: 0.8788 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01887: saving model to weights\n",
            "Epoch 1888/2000\n",
            " - 0s - loss: 0.4493 - categorical_accuracy: 0.7459 - val_loss: 0.8601 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01888: saving model to weights\n",
            "Epoch 1889/2000\n",
            " - 0s - loss: 0.4454 - categorical_accuracy: 0.7456 - val_loss: 0.9042 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01889: saving model to weights\n",
            "Epoch 1890/2000\n",
            " - 0s - loss: 0.4448 - categorical_accuracy: 0.7467 - val_loss: 0.8945 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01890: saving model to weights\n",
            "Epoch 1891/2000\n",
            " - 0s - loss: 0.4475 - categorical_accuracy: 0.7441 - val_loss: 0.8984 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01891: saving model to weights\n",
            "Epoch 1892/2000\n",
            " - 0s - loss: 0.4429 - categorical_accuracy: 0.7467 - val_loss: 1.0065 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01892: saving model to weights\n",
            "Epoch 1893/2000\n",
            " - 0s - loss: 0.5854 - categorical_accuracy: 0.7037 - val_loss: 0.9170 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01893: saving model to weights\n",
            "Epoch 1894/2000\n",
            " - 0s - loss: 0.5793 - categorical_accuracy: 0.7078 - val_loss: 1.1303 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01894: saving model to weights\n",
            "Epoch 1895/2000\n",
            " - 0s - loss: 0.5351 - categorical_accuracy: 0.7167 - val_loss: 0.9349 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01895: saving model to weights\n",
            "Epoch 1896/2000\n",
            " - 0s - loss: 0.4941 - categorical_accuracy: 0.7270 - val_loss: 0.9738 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01896: saving model to weights\n",
            "Epoch 1897/2000\n",
            " - 0s - loss: 0.4771 - categorical_accuracy: 0.7393 - val_loss: 0.9176 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01897: saving model to weights\n",
            "Epoch 1898/2000\n",
            " - 0s - loss: 0.4735 - categorical_accuracy: 0.7322 - val_loss: 0.9227 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01898: saving model to weights\n",
            "Epoch 1899/2000\n",
            " - 0s - loss: 0.4589 - categorical_accuracy: 0.7407 - val_loss: 0.9784 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01899: saving model to weights\n",
            "Epoch 1900/2000\n",
            " - 0s - loss: 0.4635 - categorical_accuracy: 0.7393 - val_loss: 1.1548 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01900: saving model to weights\n",
            "Epoch 1901/2000\n",
            " - 0s - loss: 0.5567 - categorical_accuracy: 0.7011 - val_loss: 0.9257 - val_categorical_accuracy: 0.6455\n",
            "\n",
            "Epoch 01901: saving model to weights\n",
            "Epoch 1902/2000\n",
            " - 0s - loss: 0.5428 - categorical_accuracy: 0.6985 - val_loss: 1.0932 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01902: saving model to weights\n",
            "Epoch 1903/2000\n",
            " - 0s - loss: 0.4785 - categorical_accuracy: 0.7267 - val_loss: 0.9349 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01903: saving model to weights\n",
            "Epoch 1904/2000\n",
            " - 0s - loss: 0.4597 - categorical_accuracy: 0.7396 - val_loss: 0.9154 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01904: saving model to weights\n",
            "Epoch 1905/2000\n",
            " - 0s - loss: 0.4486 - categorical_accuracy: 0.7448 - val_loss: 0.9134 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01905: saving model to weights\n",
            "Epoch 1906/2000\n",
            " - 0s - loss: 0.4664 - categorical_accuracy: 0.7326 - val_loss: 0.9644 - val_categorical_accuracy: 0.6656\n",
            "\n",
            "Epoch 01906: saving model to weights\n",
            "Epoch 1907/2000\n",
            " - 0s - loss: 0.4560 - categorical_accuracy: 0.7393 - val_loss: 1.0056 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01907: saving model to weights\n",
            "Epoch 1908/2000\n",
            " - 0s - loss: 0.4432 - categorical_accuracy: 0.7437 - val_loss: 0.9089 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01908: saving model to weights\n",
            "Epoch 1909/2000\n",
            " - 0s - loss: 0.4399 - categorical_accuracy: 0.7463 - val_loss: 0.9463 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01909: saving model to weights\n",
            "Epoch 1910/2000\n",
            " - 0s - loss: 0.4365 - categorical_accuracy: 0.7474 - val_loss: 1.0619 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01910: saving model to weights\n",
            "Epoch 1911/2000\n",
            " - 0s - loss: 0.4428 - categorical_accuracy: 0.7430 - val_loss: 0.9175 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01911: saving model to weights\n",
            "Epoch 1912/2000\n",
            " - 0s - loss: 0.4366 - categorical_accuracy: 0.7493 - val_loss: 0.9266 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01912: saving model to weights\n",
            "Epoch 1913/2000\n",
            " - 0s - loss: 0.4330 - categorical_accuracy: 0.7474 - val_loss: 1.0036 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01913: saving model to weights\n",
            "Epoch 1914/2000\n",
            " - 0s - loss: 0.4389 - categorical_accuracy: 0.7459 - val_loss: 0.9834 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01914: saving model to weights\n",
            "Epoch 1915/2000\n",
            " - 0s - loss: 0.4334 - categorical_accuracy: 0.7507 - val_loss: 0.9375 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01915: saving model to weights\n",
            "Epoch 1916/2000\n",
            " - 0s - loss: 0.4297 - categorical_accuracy: 0.7485 - val_loss: 1.0582 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01916: saving model to weights\n",
            "Epoch 1917/2000\n",
            " - 0s - loss: 0.4377 - categorical_accuracy: 0.7448 - val_loss: 0.9509 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01917: saving model to weights\n",
            "Epoch 1918/2000\n",
            " - 0s - loss: 0.4365 - categorical_accuracy: 0.7463 - val_loss: 1.0477 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01918: saving model to weights\n",
            "Epoch 1919/2000\n",
            " - 0s - loss: 0.4340 - categorical_accuracy: 0.7478 - val_loss: 0.9929 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01919: saving model to weights\n",
            "Epoch 1920/2000\n",
            " - 0s - loss: 0.4299 - categorical_accuracy: 0.7493 - val_loss: 1.0043 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01920: saving model to weights\n",
            "Epoch 1921/2000\n",
            " - 0s - loss: 0.4428 - categorical_accuracy: 0.7467 - val_loss: 1.0979 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01921: saving model to weights\n",
            "Epoch 1922/2000\n",
            " - 0s - loss: 0.6209 - categorical_accuracy: 0.6756 - val_loss: 0.9215 - val_categorical_accuracy: 0.6187\n",
            "\n",
            "Epoch 01922: saving model to weights\n",
            "Epoch 1923/2000\n",
            " - 0s - loss: 0.6555 - categorical_accuracy: 0.6552 - val_loss: 1.0359 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01923: saving model to weights\n",
            "Epoch 1924/2000\n",
            " - 0s - loss: 0.5658 - categorical_accuracy: 0.6978 - val_loss: 0.8643 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01924: saving model to weights\n",
            "Epoch 1925/2000\n",
            " - 0s - loss: 0.5013 - categorical_accuracy: 0.7267 - val_loss: 0.9768 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01925: saving model to weights\n",
            "Epoch 1926/2000\n",
            " - 0s - loss: 0.5072 - categorical_accuracy: 0.7226 - val_loss: 1.0587 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01926: saving model to weights\n",
            "Epoch 1927/2000\n",
            " - 0s - loss: 0.4829 - categorical_accuracy: 0.7311 - val_loss: 0.9370 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01927: saving model to weights\n",
            "Epoch 1928/2000\n",
            " - 0s - loss: 0.4730 - categorical_accuracy: 0.7363 - val_loss: 0.8500 - val_categorical_accuracy: 0.6823\n",
            "\n",
            "Epoch 01928: saving model to weights\n",
            "Epoch 1929/2000\n",
            " - 0s - loss: 0.4614 - categorical_accuracy: 0.7426 - val_loss: 0.9471 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01929: saving model to weights\n",
            "Epoch 1930/2000\n",
            " - 0s - loss: 0.4518 - categorical_accuracy: 0.7430 - val_loss: 0.9297 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01930: saving model to weights\n",
            "Epoch 1931/2000\n",
            " - 0s - loss: 0.4511 - categorical_accuracy: 0.7467 - val_loss: 0.9233 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01931: saving model to weights\n",
            "Epoch 1932/2000\n",
            " - 0s - loss: 0.4364 - categorical_accuracy: 0.7500 - val_loss: 0.9459 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01932: saving model to weights\n",
            "Epoch 1933/2000\n",
            " - 0s - loss: 0.4926 - categorical_accuracy: 0.7356 - val_loss: 1.0303 - val_categorical_accuracy: 0.6555\n",
            "\n",
            "Epoch 01933: saving model to weights\n",
            "Epoch 1934/2000\n",
            " - 0s - loss: 0.4949 - categorical_accuracy: 0.7289 - val_loss: 1.0976 - val_categorical_accuracy: 0.7124\n",
            "\n",
            "Epoch 01934: saving model to weights\n",
            "Epoch 1935/2000\n",
            " - 0s - loss: 0.4529 - categorical_accuracy: 0.7404 - val_loss: 0.8336 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01935: saving model to weights\n",
            "Epoch 1936/2000\n",
            " - 0s - loss: 0.4724 - categorical_accuracy: 0.7426 - val_loss: 1.0737 - val_categorical_accuracy: 0.6622\n",
            "\n",
            "Epoch 01936: saving model to weights\n",
            "Epoch 1937/2000\n",
            " - 0s - loss: 0.5197 - categorical_accuracy: 0.7270 - val_loss: 1.0034 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01937: saving model to weights\n",
            "Epoch 1938/2000\n",
            " - 0s - loss: 0.4637 - categorical_accuracy: 0.7444 - val_loss: 0.9221 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01938: saving model to weights\n",
            "Epoch 1939/2000\n",
            " - 0s - loss: 0.4492 - categorical_accuracy: 0.7448 - val_loss: 0.9145 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01939: saving model to weights\n",
            "Epoch 1940/2000\n",
            " - 0s - loss: 0.4376 - categorical_accuracy: 0.7500 - val_loss: 0.9845 - val_categorical_accuracy: 0.7157\n",
            "\n",
            "Epoch 01940: saving model to weights\n",
            "Epoch 1941/2000\n",
            " - 0s - loss: 0.4329 - categorical_accuracy: 0.7500 - val_loss: 0.9467 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01941: saving model to weights\n",
            "Epoch 1942/2000\n",
            " - 0s - loss: 0.4334 - categorical_accuracy: 0.7515 - val_loss: 1.0599 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01942: saving model to weights\n",
            "Epoch 1943/2000\n",
            " - 0s - loss: 0.4269 - categorical_accuracy: 0.7511 - val_loss: 0.9961 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01943: saving model to weights\n",
            "Epoch 1944/2000\n",
            " - 0s - loss: 0.4278 - categorical_accuracy: 0.7515 - val_loss: 1.0278 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01944: saving model to weights\n",
            "Epoch 1945/2000\n",
            " - 0s - loss: 0.4417 - categorical_accuracy: 0.7478 - val_loss: 1.0611 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01945: saving model to weights\n",
            "Epoch 1946/2000\n",
            " - 0s - loss: 0.4728 - categorical_accuracy: 0.7415 - val_loss: 1.2848 - val_categorical_accuracy: 0.6488\n",
            "\n",
            "Epoch 01946: saving model to weights\n",
            "Epoch 1947/2000\n",
            " - 0s - loss: 0.4972 - categorical_accuracy: 0.7367 - val_loss: 1.0832 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01947: saving model to weights\n",
            "Epoch 1948/2000\n",
            " - 0s - loss: 0.5229 - categorical_accuracy: 0.7059 - val_loss: 0.8908 - val_categorical_accuracy: 0.6288\n",
            "\n",
            "Epoch 01948: saving model to weights\n",
            "Epoch 1949/2000\n",
            " - 0s - loss: 0.5167 - categorical_accuracy: 0.7111 - val_loss: 0.8216 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01949: saving model to weights\n",
            "Epoch 1950/2000\n",
            " - 0s - loss: 0.4894 - categorical_accuracy: 0.7233 - val_loss: 0.9025 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 01950: saving model to weights\n",
            "Epoch 1951/2000\n",
            " - 0s - loss: 0.4772 - categorical_accuracy: 0.7300 - val_loss: 0.9394 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01951: saving model to weights\n",
            "Epoch 1952/2000\n",
            " - 0s - loss: 0.4710 - categorical_accuracy: 0.7333 - val_loss: 0.9356 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01952: saving model to weights\n",
            "Epoch 1953/2000\n",
            " - 0s - loss: 0.4614 - categorical_accuracy: 0.7370 - val_loss: 0.9041 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01953: saving model to weights\n",
            "Epoch 1954/2000\n",
            " - 0s - loss: 0.4565 - categorical_accuracy: 0.7381 - val_loss: 0.9510 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01954: saving model to weights\n",
            "Epoch 1955/2000\n",
            " - 0s - loss: 0.4498 - categorical_accuracy: 0.7393 - val_loss: 0.9580 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01955: saving model to weights\n",
            "Epoch 1956/2000\n",
            " - 0s - loss: 0.4461 - categorical_accuracy: 0.7404 - val_loss: 0.9492 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01956: saving model to weights\n",
            "Epoch 1957/2000\n",
            " - 0s - loss: 0.4418 - categorical_accuracy: 0.7433 - val_loss: 0.9485 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01957: saving model to weights\n",
            "Epoch 1958/2000\n",
            " - 0s - loss: 0.4408 - categorical_accuracy: 0.7448 - val_loss: 0.9088 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01958: saving model to weights\n",
            "Epoch 1959/2000\n",
            " - 0s - loss: 0.4366 - categorical_accuracy: 0.7444 - val_loss: 0.9560 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01959: saving model to weights\n",
            "Epoch 1960/2000\n",
            " - 0s - loss: 0.4377 - categorical_accuracy: 0.7459 - val_loss: 1.0045 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01960: saving model to weights\n",
            "Epoch 1961/2000\n",
            " - 0s - loss: 0.4335 - categorical_accuracy: 0.7474 - val_loss: 1.0052 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01961: saving model to weights\n",
            "Epoch 1962/2000\n",
            " - 0s - loss: 0.4355 - categorical_accuracy: 0.7448 - val_loss: 0.9848 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01962: saving model to weights\n",
            "Epoch 1963/2000\n",
            " - 0s - loss: 0.4364 - categorical_accuracy: 0.7452 - val_loss: 0.8716 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01963: saving model to weights\n",
            "Epoch 1964/2000\n",
            " - 0s - loss: 0.4889 - categorical_accuracy: 0.7333 - val_loss: 1.0297 - val_categorical_accuracy: 0.6589\n",
            "\n",
            "Epoch 01964: saving model to weights\n",
            "Epoch 1965/2000\n",
            " - 0s - loss: 0.4778 - categorical_accuracy: 0.7374 - val_loss: 0.8651 - val_categorical_accuracy: 0.7057\n",
            "\n",
            "Epoch 01965: saving model to weights\n",
            "Epoch 1966/2000\n",
            " - 0s - loss: 0.4587 - categorical_accuracy: 0.7400 - val_loss: 0.9464 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01966: saving model to weights\n",
            "Epoch 1967/2000\n",
            " - 0s - loss: 0.4411 - categorical_accuracy: 0.7467 - val_loss: 0.8698 - val_categorical_accuracy: 0.7090\n",
            "\n",
            "Epoch 01967: saving model to weights\n",
            "Epoch 1968/2000\n",
            " - 0s - loss: 0.4364 - categorical_accuracy: 0.7463 - val_loss: 0.9393 - val_categorical_accuracy: 0.7023\n",
            "\n",
            "Epoch 01968: saving model to weights\n",
            "Epoch 1969/2000\n",
            " - 0s - loss: 0.4322 - categorical_accuracy: 0.7448 - val_loss: 0.9197 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01969: saving model to weights\n",
            "Epoch 1970/2000\n",
            " - 0s - loss: 0.4365 - categorical_accuracy: 0.7459 - val_loss: 0.9888 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01970: saving model to weights\n",
            "Epoch 1971/2000\n",
            " - 0s - loss: 0.4293 - categorical_accuracy: 0.7489 - val_loss: 0.9603 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01971: saving model to weights\n",
            "Epoch 1972/2000\n",
            " - 0s - loss: 0.4256 - categorical_accuracy: 0.7511 - val_loss: 0.9158 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01972: saving model to weights\n",
            "Epoch 1973/2000\n",
            " - 0s - loss: 0.4269 - categorical_accuracy: 0.7500 - val_loss: 1.0003 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01973: saving model to weights\n",
            "Epoch 1974/2000\n",
            " - 0s - loss: 0.4243 - categorical_accuracy: 0.7522 - val_loss: 0.9604 - val_categorical_accuracy: 0.6990\n",
            "\n",
            "Epoch 01974: saving model to weights\n",
            "Epoch 1975/2000\n",
            " - 0s - loss: 0.4237 - categorical_accuracy: 0.7511 - val_loss: 1.0594 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01975: saving model to weights\n",
            "Epoch 1976/2000\n",
            " - 0s - loss: 0.4260 - categorical_accuracy: 0.7519 - val_loss: 0.9438 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01976: saving model to weights\n",
            "Epoch 1977/2000\n",
            " - 0s - loss: 0.4208 - categorical_accuracy: 0.7519 - val_loss: 1.0235 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01977: saving model to weights\n",
            "Epoch 1978/2000\n",
            " - 0s - loss: 0.4205 - categorical_accuracy: 0.7526 - val_loss: 1.0310 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01978: saving model to weights\n",
            "Epoch 1979/2000\n",
            " - 0s - loss: 0.4209 - categorical_accuracy: 0.7519 - val_loss: 1.0596 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01979: saving model to weights\n",
            "Epoch 1980/2000\n",
            " - 0s - loss: 0.4202 - categorical_accuracy: 0.7522 - val_loss: 1.0136 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01980: saving model to weights\n",
            "Epoch 1981/2000\n",
            " - 0s - loss: 0.4192 - categorical_accuracy: 0.7519 - val_loss: 1.0884 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01981: saving model to weights\n",
            "Epoch 1982/2000\n",
            " - 0s - loss: 0.4173 - categorical_accuracy: 0.7530 - val_loss: 1.0404 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01982: saving model to weights\n",
            "Epoch 1983/2000\n",
            " - 0s - loss: 0.4176 - categorical_accuracy: 0.7522 - val_loss: 1.0231 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01983: saving model to weights\n",
            "Epoch 1984/2000\n",
            " - 0s - loss: 0.4164 - categorical_accuracy: 0.7541 - val_loss: 1.1066 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01984: saving model to weights\n",
            "Epoch 1985/2000\n",
            " - 0s - loss: 0.4166 - categorical_accuracy: 0.7537 - val_loss: 1.0880 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01985: saving model to weights\n",
            "Epoch 1986/2000\n",
            " - 0s - loss: 0.4156 - categorical_accuracy: 0.7544 - val_loss: 1.0986 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01986: saving model to weights\n",
            "Epoch 1987/2000\n",
            " - 0s - loss: 0.4176 - categorical_accuracy: 0.7530 - val_loss: 1.0073 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01987: saving model to weights\n",
            "Epoch 1988/2000\n",
            " - 0s - loss: 0.4157 - categorical_accuracy: 0.7544 - val_loss: 1.1089 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01988: saving model to weights\n",
            "Epoch 1989/2000\n",
            " - 0s - loss: 0.4152 - categorical_accuracy: 0.7548 - val_loss: 1.1281 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01989: saving model to weights\n",
            "Epoch 1990/2000\n",
            " - 0s - loss: 0.4132 - categorical_accuracy: 0.7533 - val_loss: 1.1325 - val_categorical_accuracy: 0.6890\n",
            "\n",
            "Epoch 01990: saving model to weights\n",
            "Epoch 1991/2000\n",
            " - 0s - loss: 0.4154 - categorical_accuracy: 0.7581 - val_loss: 1.1028 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01991: saving model to weights\n",
            "Epoch 1992/2000\n",
            " - 0s - loss: 0.4512 - categorical_accuracy: 0.7544 - val_loss: 1.1592 - val_categorical_accuracy: 0.6789\n",
            "\n",
            "Epoch 01992: saving model to weights\n",
            "Epoch 1993/2000\n",
            " - 0s - loss: 0.4535 - categorical_accuracy: 0.7478 - val_loss: 1.0716 - val_categorical_accuracy: 0.6522\n",
            "\n",
            "Epoch 01993: saving model to weights\n",
            "Epoch 1994/2000\n",
            " - 0s - loss: 0.4448 - categorical_accuracy: 0.7470 - val_loss: 1.0442 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01994: saving model to weights\n",
            "Epoch 1995/2000\n",
            " - 0s - loss: 0.4414 - categorical_accuracy: 0.7507 - val_loss: 1.0731 - val_categorical_accuracy: 0.6856\n",
            "\n",
            "Epoch 01995: saving model to weights\n",
            "Epoch 1996/2000\n",
            " - 0s - loss: 0.4312 - categorical_accuracy: 0.7548 - val_loss: 1.1884 - val_categorical_accuracy: 0.6689\n",
            "\n",
            "Epoch 01996: saving model to weights\n",
            "Epoch 1997/2000\n",
            " - 0s - loss: 0.4343 - categorical_accuracy: 0.7519 - val_loss: 1.0236 - val_categorical_accuracy: 0.6957\n",
            "\n",
            "Epoch 01997: saving model to weights\n",
            "Epoch 1998/2000\n",
            " - 0s - loss: 0.4303 - categorical_accuracy: 0.7559 - val_loss: 1.0943 - val_categorical_accuracy: 0.6923\n",
            "\n",
            "Epoch 01998: saving model to weights\n",
            "Epoch 1999/2000\n",
            " - 0s - loss: 0.5427 - categorical_accuracy: 0.7289 - val_loss: 1.0637 - val_categorical_accuracy: 0.6355\n",
            "\n",
            "Epoch 01999: saving model to weights\n",
            "Epoch 2000/2000\n",
            " - 0s - loss: 0.4916 - categorical_accuracy: 0.7222 - val_loss: 1.1891 - val_categorical_accuracy: 0.6756\n",
            "\n",
            "Epoch 02000: saving model to weights\n",
            "evaluate the model - train_set:\n",
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 9)                 0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 512)               5120      \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 64)                32832     \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 9)                 585       \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 3)                 30        \n",
            "=================================================================\n",
            "Total params: 38,567\n",
            "Trainable params: 38,567\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc4agaCQyn4O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "65d4ebc6-f7a7-4b25-8de0-ce3a26d693b9"
      },
      "source": [
        "print(\"保存调制识别模型 \\n\")\n",
        "    \n",
        "model.save('modulationModel.h5')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "保存调制识别模型 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtjuRezKulkK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  # test model\n",
        "    LOW = -10\n",
        "    HIGH = 20\n",
        "    GAP = 2\n",
        "    NTest = 100\n",
        "    test_data = np.loadtxt('./test_cumulant.csv', delimiter = ',', dtype = float)\n",
        "    xTest = test_data[:,0:Nfeature]\n",
        "    yTest = test_data[:,Nfeature]\n",
        "    yTest.astype(int)\n",
        "    yPredict = model.predict(xTest)\n",
        "    predict_curve = np.zeros((2, int((HIGH-LOW)/GAP) + 1)) # snr + Pc\n",
        "    #第一个for循环得到x轴的刻度\n",
        "    for i in range(predict_curve.shape[1]): # snr - [-10 -8 ... 18 20]\n",
        "        predict_curve[0, i] = LOW + GAP * i\n",
        "    #不同信噪比的信号 预测正确的个数\n",
        "    for i in range(test_data.shape[0]): # Pc - count num [241 431 ... 3900 4000 4000]\n",
        "        snr_loc = int((test_data[i, Nfeature+1] - LOW)/GAP);\n",
        "        if yTest[i] == np.argmax(yPredict[i,:]):\n",
        "            predict_curve[1, snr_loc] = predict_curve[1, snr_loc] + 1\n",
        "    #不同信噪比预测正确的百分比，结果应该为噪声越大，预测准确率越低，信噪比越高，预测准确率越高\n",
        "    for i in range(predict_curve.shape[1]): # Pc - cal pc(cnt_num/sum_num 3900/4000)\n",
        "        predict_curve[1, i] = predict_curve[1, i]/(NTest*NClass)\n",
        "        \n",
        "    #np.savetxt('CUM_NN_L100.txt', predict_curve, delimiter=',', fmt='%.6f')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QheAPZ8qnYhL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "85ee9558-4791-4c5c-b905-fed15af7a573"
      },
      "source": [
        "print(xTest)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3.3780e+00 3.0700e+01 2.0065e+01 ... 1.3146e+01 4.7603e+01 2.7853e+01]\n",
            " [5.7661e+00 4.1051e+01 1.6567e+01 ... 4.5010e+01 3.0656e+01 5.9492e-02]\n",
            " [5.6692e+00 2.6695e+01 7.9369e+00 ... 1.8400e+01 1.0605e+01 2.8180e+00]\n",
            " ...\n",
            " [1.8393e-03 1.0745e-02 4.4017e-01 ... 1.2715e+02 8.4533e+01 2.2538e+02]\n",
            " [4.9659e-04 9.3519e-03 8.5968e-01 ... 3.0239e+02 4.9546e+01 2.1872e+02]\n",
            " [7.7513e-04 9.7297e-03 8.2425e-01 ... 2.6728e+02 7.4578e+01 2.0519e+02]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ktSoLMPnSyc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "ab52b0dd-5dc3-408c-888d-08d3103a2212"
      },
      "source": [
        "print(yPredict)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.35879132 0.29188824 0.34932038]\n",
            " [0.35879132 0.29188824 0.34932038]\n",
            " [0.35879132 0.29188824 0.34932038]\n",
            " ...\n",
            " [0.         0.         1.        ]\n",
            " [0.         0.         1.        ]\n",
            " [0.         0.         1.        ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4TU1J4XRnCKN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "outputId": "f7ad6db8-e0de-413a-9642-e0f60c3f45a8"
      },
      "source": [
        "\n",
        "print(predict_curve)\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-b082b83bf5c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredict_curve\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'predict_curve' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3QJaUSNmwls",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "33a453ed-1afc-4734-fd73-93c58c4782ce"
      },
      "source": [
        "#输入测试集计算预测的准确性，可以看到snr 在5db之下的时候，由于噪声过大，模型识别正确率很低\n",
        "#当SNR大于等于6db的时候，模型接近百分之百预测正确信号的分类\n",
        "plt.plot(predict_curve[0], predict_curve[1], 'o-')\n",
        "\n",
        "plt.grid(True)\n",
        "#plt.legend(loc='lower right')\n",
        "plt.xlim((LOW, HIGH))\n",
        "plt.ylim((0,1))\n",
        "plt.xlabel('SNR(dB)')\n",
        "plt.ylabel('Pc')\n",
        "plt.title('Modulation Classification Prediction Accuracy')\n",
        "plt.savefig('modulation_accuracy.png', format='png')\n",
        "#res = np.vstack((snr, acc))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_D0cpZ5KvDUJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#一个是保存模型用于之后调用 另一个是要把各个不同SNR的图画出来"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zng9yUx0ymU0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}